Sounding the alarm:Functionally referential signaling in Azure-winged Magpie

Functionally referential signals are a complex form of communication that conveys information about the external environment.Such signals have been found in a range of mammal and bird species and have helped us understand the complexities of animal communication.Corvids are well known for their extraordinary cognitive abilities,but relatively little attention has been paid to their vocal function.Here,we investigated the functionally referential signals of a cooperatively breeding corvid species,Azure-winged Magpie(Cyanopica cyanus).Through field observations,we suggest that Azure-winged Magpie uses referential alarm calls to distinguish two types of threats:’rasp’ calls for terrestrial threats and ’chatter’ calls for aerial threats.A playback experiment revealed that Azure-winged Magpies responded to the two call types with qualitatively different behaviors.They sought cover by flying into the bushes in response to the ’chatter’ calls,and flew to or stayed at higher positions in response to ’rasp’ calls,displaying a shorter response time to ’chatter’ calls.Significant differences in acoustic structure were found between the two types of calls.Given the extensive cognitive abilities of corvids and the fact that referential signals were once thought to be unique to primates,these findings are important for expanding our understanding of social communication and language evolution.

Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm floods.First,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm occurrences.Then,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via clustering.The effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

Glass-compatible and self-powered temperature alarm system by temperature-responsive organic manganese halides via backward energy transfer process

A pioneering glass-compatible transparent temperature alarm system self-powered by luminescent solar concentrators(LSCs) is reported.Single green-emitted organic manganese halides(OMHs) of PEA_(2)MnBr_(2)I_(2),which has a unique temperature-dependent backward energy transfer process from selftrapped state to^(4)T_(1)energy level of Mn,is used for triggering the temperature alarm.The LSC with redemitted CsPbI_(3)perovskite-polymer composite films on the glass substrate is used for power supply.The spectrally separated nature between the green-emitted OMHs for temperature alarm and red-emitted CsPbI3in LSC for power supply allows for probing the signal light of temperature-responsive OMHs without the interference of LSCs,making it possible to calibrate the temperature visually just by a self-powered brightness detection circuit with LED indicators.Taking advantage of LSC without hot spot effects plaguing the solar cells,as-prepared temperature alarm system can operate well on both sunny and cloudy day.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

Enhancing Feature Discretization in Alarm and Fire Detection Systems Using Probabilistic Inference Models

Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.

Predictive value of alarm features in diagnosing upper gastrointestinal malignancies among dyspeptic patients:A cross-sectional study in Ethiopia

Objective:The study aimed to determine the overall predictive value of alarm features in diagnosing upper Gastrointestinal(GI)malignancies and significant endoscopic findings among patients undergoing elective Esophagogastroduodenoscopy(EGD)at Tikur Anbessa Special Hospital(TASH)and Adera Medical Centre(AMC).Methods:It was an institution-based cross-sectional study conducted on patients undergoing elective endoscopy for an upper GI complaint from July to September 2022.Data was collected from patient charts,and biopsies were taken for histologic confirmation.The study assessed the association of alarm symptoms and signs with significant upper gastrointestinal(UGI)endoscopic findings and malignancies.Results:142 patients were selected,with an average age of 48.35 and 52.1% being male.Epigastric pain was the most common reason for an endoscopy.62% of patients had at least one alarm feature,the most common being unexplained weight loss and UGI bleeding.The study found a strong association between the presence of alarm features,significant endoscopic findings,and UGI malignancies.The pooled sensitivity and specificity of any alarm feature for any significant finding were 79% and 64.9%,respectively,and for malignancy,100% and 39.7%,respectively.The presence of the alarm feature was associated with an increase of 6.801 in the odds of developing SEF and an increase of 4.199 in the odds of developing malignancy.Conclusions:UGI alarm symptoms and signs like an abdominal mass,persistent vomiting,dysphagia,and UGI bleeding are predictive of significant endoscopic findings and malignancies.Hence,EGD should be done and suspicious lesions should be biopsied early,regardless of gender,age,or duration of symptoms.

费森4008B血透机Flow alarm故障维修

1故障现象 机器运行过程中电导度不稳定,上下漂移且出现Flow alarm报警。

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Function of note strings in Japanese Tit alarm calls to the Common Cuckoo:a playback experiment

Background: Birds produce alarm calls to convey information about threats. Some Passerine alarm calls consist of several note strings, but few studies have examined their function. Previous studies have shown that Japanese Tits(Parus minor) can alter the calling rate and number and combination of notes in response to predators. We previously found the combinations of note types in Japanese Tit alarm calls to be significantly different in response to the Sparrowhawk(Accipiter nisus) and Common Cuckoo(Cuculus canorus).Methods: Through playback experiments, we tested whether the note strings in Japanese Tit alarm calls to the Common Cuckoo have different functions in conveying information. The note strings of selected alarm calls were divided into the categories of C and D, and different calls were then constructed separately based on the two note string categories. Original alarm calls(C–D), C calls and D calls were played back to male Japanese Tits during the incubation period.Results: Male Japanese Tits had a significantly stronger response to C calls than to C–D calls, and they showed a significantly stronger response to both C and C–D calls than to D calls, suggesting that Japanese Tits discriminated between the C and D calls.Conclusions: Our study demonstrated that the C-and D-category note strings of Japanese Tit alarm calls to the Common Cuckoo have different functions, which supports the previous finding that different note strings in an alarm call can provide different information to receivers. However, the exact meanings of these note strings are not yet known, and further investigation is therefore required.

Predictive value of alarm symptoms in patients with Rome IV dyspepsia: A cross-sectional study

BACKGROUND No studies have evaluated the predictive value of alarm symptoms for organic dyspepsia and organic upper gastrointestinal(GI)diseases based on Rome IV criteria in the Chinese population.AIM To evaluate the predictive value of alarm symptoms for dyspeptic patients based on Rome IV criteria.METHODS We performed a cross-sectional study of dyspepsia patients who met the inclusion and exclusion criteria at two academic urban tertiary-care centers from March 2018 to January 2019.Basic demographic data,dyspeptic information,alarm symptoms,lifestyle,examination results,family history and outpatient cost information were collected.Dyspepsia patients with normal findings on upper GI endoscopy,epigastric ultrasound and laboratory examination and without Helicobacter pylori-associated dyspepsia were classified as functional dyspepsia.RESULTS A total of 381 patients were enrolled in the study,including 266 functional dyspepsia patients and 115 organic dyspepsia patients.There were 24 patients with organic upper GI disease among patients with organic dyspepsia.We found that based on the Rome IV criteria,alarm symptoms were of limited value in differentiating organic dyspepsia and organic upper GI diseases from functional dyspepsia.Age(odds ratio(OR)=1.056,P=0.012),smoking(OR=4.714,P=0.006)and anemia(OR=88.270,P<0.001)were independent predictors for organic upper GI diseases.For the comparison of epigastric pain syndrome,postprandial distress syndrome and epigastric pain syndrome combined with postprandial distress syndrome,the results showed that there were statistically significant differences in anorexia(P=0.021)and previous visits(P=0.012).The ClinicalTrials.gov number is NCT 03479528.CONCLUSION Most alarm symptoms had poor predictive value for organic dyspepsia and organic upper GI diseases based on Rome IV criteria.Gastroscopic screening should not be based solely on alarm symptoms.

Responses of cuckoo hosts to alarm signals of different nest intruders in non-nesting areas

DEAR EDITOR,The“call for help”hypothesis proposes that alarm calls produced by a bird can transmit warning information to both conspecific and interspecific neighbors.Neighbors who are attracted by social transmission might benefit from knowing about the presence of danger or by gaining information about the presence of predators or brood parasites nearby.Brood parasite hosts can distinguish threats from different intruders and exhibit varied responses correspondingly.

Constant False Alarm Rate Acquisition Algorithm for Compass B1C Signal

In order to improve the sensitivity of the Compass B1C signal acquisition for the receiver,the principle of constant false alarm rate(CFAR)is applied for the B1C pilot channel acquisition to realize the dynamic adjustment of the threshold of acquisition against the carrier to noise ratio.The non-coherent data/pilot combined acquisition algorithm for B1C signal is analyzed to make full use of the power of the B1C signal under the condition of low carrier to noise ratio.On this basis,to improve the acquisition sensitivity of the receiver,the principle of constant false alarm probability is applied for the non-coherent data/pilot combined acquisition algorithm.Theoretical analysis and simulations show that the non-coherent data/pilot combined acquisition algorithm with CFAR improves the B1C signal acquisition sensitivity of the receiver significantly,and achieves a better Receiver Operating Characteristic compared with the traditional acquisition algorithms.

An ECG Monitoring and Alarming System Based On Android Smart Phone

ECG monitoring in daily life is an important means of treating heart disease. To make it easier for the medical to monitor the ECG of their patients outside the hospital, we designed and developed an ECG monitoring and alarming system based on Android smart phone. In our system, an ECG device collects the ECG signal and transmits it to an Android phone. The Android phone detects alarms which come from the ECG devices. When alarms occur, Android phone will capture the ECG images and the details about the alarms, and sends them to the cloud Alarm Server (AS). Once received, AS push the messages to doctors’ phone, so the doctors could see the ECG images and alarm details on their mobile phone. In our system, high resolution ECG pictures are transmitted to doctors’ phone in a user-friendly way, which can help doctors keep track of their patient’s condition easily.

Fire-Detectors Review and Design of an Automated, Quick Responsive Fire-Alarm System Based on SMS

In this work a review of existing fire-detector types has been carried out along with the development of a low cost, portable, and reliable microcontroller based automated fire alarm system for remotely alerting any fire incidents in household or industrial premises. The aim of the system designed is to alert the distant property-owner efficiently and quickly by sending short message (SMS) via GSM network. A Linear integrated temperature sensor detects temperature beyond preset value whereas semiconductor type sensor detects presence of smoke or gas from fire hazards. The sensor units are connected via common data line to ATMega8L AVR microcontroller. A SIM300CZ GSM kit based network module, capable of operating in standard GSM bands, has been used to send alert messages. The system is implemented on printed circuit board (PCB) and tested under different experimental conditions to evaluate its performances.

Knowledge Discovery from Communication Network Alarm Databases

The technique of Knowlege Discovery in Databases (KDD) to learn valuable knowledge hidden in network alarm databases is introduced. To get such knowledge, we propose an efficient method based on sliding windows (named as Slidwin) to discover different episode rules from time squential alarm data. The experimental results show that given different thresholds parameters, large amount of different rules could be discovered quickly.

A Wireless Sensor Network Ad-Hoc Designed as Anti-Theft Alarm System for Photovoltaic Panels

Photovoltaic (PV) systems have attracted increasing attention in last years as well as Wireless Sensor Networks (WSNs), which have been used in many application fields. In PV plants, especially in ground installations, a lot of thefts and damages occur due to the still high cost of the modules. A new experimental WSN ad-hoc has been designed to be an anti-theft alarm system. Each node of the network is directly installed under each PV string and it is equipped with an accelerometer sensor capable to detect a minimum displacement of the panel from its steady position. The WSN presents a star topology: a master node cyclically interrogates the slave nodes through RF link. It collects all the nodes responses and communicates though a RS-232 interface with a control PC checking the network status. When a slave node detects an alarm, continuous messages are sent to the control PC which turns on all the alarm signaling systems. The control PC is equipped with an open source operative system and software and provides for SMS, e-mail and sound-light signaling in case of alarm. It also communicates with a remote server where all the WSN information is stored. A first low cost experimental WSN has been already installed and it is working properly.

一种基于神经网络的alarm2vec告警压缩算法

告警压缩是在网络故障告警的相关性分析中,发现告警间的关联性,从而减少告警数量,获取有用信息,根据已建立的告警知识库快速准确地对其进行分析推理,获取根源故障,从而确定网络设备或网络功能故障,完成故障定位。针对该场景,提出了一种基于神经网络的alarm2vec算法,并结合极大团图挖掘告警间的关联规则,实际网络告警数据实验结果表明,该方法相对原始数据,实现39.85%的告警压缩率,对告警数据信息进行了有效压缩。

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.