Detection System
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2021 ◽  
Vol 21 (5) ◽  
pp. 115-121
Author(s):  
Younghyun Kim ◽  
Boseb Kim ◽  
Jongjin Jung

In the event of fire, how quickly occupants can hear, see, and/or smell the fire and then exit the building are important for reducing the number of potential casualties. After a person or an automatic fire-detection system detects a fire, an installed emergency alarm system is used to alert building occupants about the fire. The emergency alarm system plays an important role in alerting the occupants to the fire by emitting a high-pitched sound when the fire is initially detected. Although bells and electronic sirens can both be used in fire-alarm systems, usually only bells are used in most commercial fire alarms except for a few fire extinguishers. Recently, however, the development of circuit integration technology and subsequent competitive pricing and improved performance have fostered an environment favorable for the widespread application of electronic sirens. However, because electronic sirens that emit various sounds will likely confuse building occupants used to hearing familiar-sounding conventional fire-alarm bells, electronic sirens must be engineered to sound like conventional fire-alarm bells. Therefore, in this study, experiments were conducted to measure the specific sound pressure and frequency characteristics of commercially available fire-alarm bells and electronic sirens, and their characteristics were reviewed. In addition, the differences between the bells and sirens were analyzed to develop a plan for supplementing warning sounds of electronic sirens.


2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.


2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yue Xiao ◽  
Yan Li ◽  
Changbao Chu

In this paper, we analyze the performance of mechanical equipment through a closed-loop feedback health monitoring vibration sensor, develop an OTDR optical signal reception and the processing module, and realize the reception, amplification, and filtering of the backscattered optical signal. In terms of vibration signal demodulation, the FPGA signal processing module was developed and debugged to realize the intermodulation with OTDR optical signal reception processing module and the preprocessing of the vibration data stream by taking advantage of the FPGA in parallel high-speed data stream processing. The objective function is constructed based on the dynamic data of the first four vertical frequencies of the modal recognition and the static data of the constant-load cable force of the inclined cable, and the third-order response surface method is applied to fit the response surface function of each correction target. The errors between the corrected FEM calculated values and the measured results are within 5%. The results were compared with the results of static and dynamic corrections, and the results showed that the joint static and dynamic corrections using the third-order response surface could obtain a finite element model that was more comprehensive and closer to the actual engineering response. A 180° feedback gain is set in the mass detection system to reduce the system’s equivalent mass and increase the system resonant frequency. An inverse lock-in amplifier is used instead of a high-frequency bandpass filter to spectrally migrate the useful frequencies and better filter out noise interference. A thin-film microresonant pressure sensor, a cantilever beam microresonant gas sensor, and a microresonant biosensor were designed and developed using the micromachining process. A closed-loop feedback method was used to design a low-frequency detection system, a medium-frequency detection system, and a high-frequency feedback detection based on a phase-locked loop system, completed open-loop and closed-loop detection experiments of the intrinsic frequency of the sensor, through-pressure experiments of the pressure sensor, low and medium frequency gas-sensitive experiments of the gas sensor, and high-frequency detection experiments of the biosensor oxygen absorption/deoxygenation, and measured the mass of individual oxygen molecules.


Author(s):  
Changxin Lai ◽  
Shijie Zhou ◽  
Natalia A. Trayanova

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models. In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple common ECG abnormality types. The data analysed in this study consisted of 6877 12-lead ECG recordings from CPSC 2018 (labelled as normal rhythm or eight types of ECG abnormalities, split into training (approx. 80%), validation (approx. 10%) and test (approx. 10%) sets) and 3998 12-lead ECG recordings from PhysioNet/CinC 2020 (labelled as normal rhythm or four types of ECG abnormalities, used as external text set). The ECG-lead subset selection module was introduced within the proposed model to efficiently constrain model complexity. It detected an optimal 4-lead ECG subset consisting of leads II, aVR, V1 and V4. The proposed model using the optimal 4-lead subset significantly outperformed the model using the complete 12-lead ECG on the validation set and on the external test dataset. The results demonstrated that our proposed model successfully identified an optimal subset of 12-lead ECG; the resulting 4-lead ECG subset improves the generalizability of the DL model in ECG abnormality interpretation. This study provides an outlook on what channels are necessary to keep and which ones may be ignored when considering an automated detection system for cardiac ECG abnormalities. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.


Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6432
Author(s):  
Lei Wang ◽  
Xing Dai ◽  
Yujian Feng ◽  
Qiyang Zhao ◽  
Lin Liu ◽  
...  

MicroRNA160 plays a crucial role in plant development by negatively regulating the auxin response factors (ARFs). In this manuscript, we design an automatic molecule machine (AMM) based on the dual catalytic hairpin assembly (D-CHA) strategy for the signal amplification detection of miRNA160. The detection system contains four hairpin-shaped DNA probes (HP1, HP2, HP3, and HP4). For HP1, the loop is designed to be complementary to miRNA160. A fragment of DNA with the same sequences as miRNA160 is separated into two pieces that are connected at the 3′ end of HP2 and 5′ end of HP3, respectively. In the presence of the target, four HPs are successively dissolved by the first catalytic hairpin assembly (CHA1), forming a four-way DNA junction (F-DJ) that enables the rearrangement of separated DNA fragments at the end of HP2 and HP3 and serving as an integrated target analogue for initiating the second CHA reaction, generating an enhanced fluorescence signal. Assay experiments demonstrate that D-CHA has a better performance compared with traditional CHA, achieving the detection limit as low as 10 pM for miRNA160 as deduced from its corresponding DNA surrogates. Moreover, non-target miRNAs, as well as single-base mutation targets, can be detected. Overall, the D-CHA strategy provides a competitive method for plant miRNAs detection.


PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258880
Author(s):  
Chang Hee Han ◽  
Eal Kim ◽  
Tan Nhu Nhat Doan ◽  
Dongil Han ◽  
Seong Joon Yoo ◽  
...  

Background Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images. Methods Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps. Results The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN. Conclusions The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.


2021 ◽  
Vol 11 (11) ◽  
pp. 1393
Author(s):  
Saugat Bhattacharyya ◽  
Mitsuhiro Hayashibe

 This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it. 


2021 ◽  
Vol 2 (4) ◽  
pp. 1-26
Author(s):  
Jassim Happa ◽  
Thomas Bashford-Rogers ◽  
Alastair Janse Van Rensburg ◽  
Michael Goldsmith ◽  
Sadie Creese

In this article, we propose a novel method that aims to improve upon existing moving-target defences by making them unpredictably reactive using probabilistic decision-making. We postulate that unpredictability can improve network defences in two key capacities: (1) by re-configuring the network in direct response to detected threats, tailored to the current threat and a security posture, and (2) by deceiving adversaries using pseudo-random decision-making (selected from a set of acceptable set of responses), potentially leading to adversary delay and failure. Decisions are performed automatically, based on reported events (e.g., Intrusion Detection System (IDS) alerts), security posture, mission processes, and states of assets. Using this codified form of situational awareness, our system can respond differently to threats each time attacker activity is observed, acting as a barrier to further attacker activities. We demonstrate feasibility with both anomaly- and misuse-based detection alerts, for a historical dataset (playback), and a real-time network simulation where asset-to-mission mappings are known. Our findings suggest that unpredictability yields promise as a new approach to deception in laboratory settings. Further research will be necessary to explore unpredictability in production environments.


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