Off-line Detection Research Articles

Dimensionality reduction for off-line object recognition and detection using supervised learning

Dimensionality reduction for off-line object recognition and detection using supervised learning

Bioassay-Guided Fractionation of Anti-Leishmania Amazonensis Extracts from Ampelozizyphus Amazonicus by High-Speed Countercurrent Chromatography.

Ampelozizyphus amazonicus Ducke, known as "saracura-mirá" in the Amazon rainforest, is valued for its traditional use in malaria prevention and treatment. The plant's roots and bark are employed for these purposes, while the wood is often overlooked. Given the global importance of leishmaniasis, this research focused on A. amazonicus anti-Leishmania amazonensis potential. The ethyl acetate extract from the bark (EAEB) exhibited the most effective inhibition of intracellular amastigote growth with IC50 7.0 μg mL-1 but showed high toxicity (CC50 9.0 μg mL-1). The wood ethanol (EW) and ethyl acetate (EAEW) extracts demonstrated promising results against L. amazonensis with IC50 15.8 μg mL-1 and 10.0 μg mL-1, CC50 50.5 μg mL-1 and 22.7 μg mL-1, respectively, with EW showing the greatest promise. High-speed countercurrent chromatography (HSCCC) combined with mass spectrometry (MS) off-line detection was employed for the bioassay-guided fractionation of EAEB and EAEW using HEMWat as a solvent system. These extracts showed to be rich in saponins and triterpenes, besides the presence of glycosylated flavonoids recently reported as minor compounds. The fractions obtained from HSCCC showed high toxicity. However, this technique proved effective in obtaining fractions with a high purity level, leading to the identification of a di-glycosylated triterpene saponin from EAEB, and melaleucic acid from EAEW. These findings highlight A. amazonicus as a potential source for developing new therapeutic alternatives for leishmaniasis and HSCCC as a technique enabling better separation efficiency of complex saponin matrices.

Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest

The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.

Development of a Monitoring System for Huangjiu Storage based on Electrical Conductivity

Abstract In this study, a fast rancidification detection method was developed for Huangjiu in pottery jars based on the principle of electrical conductivity changes caused by microbial contamination. The change of total acid in Huangjiu was positively correlated with the increase of electrical conductivity. This method was applied to an online monitoring system for Huangjiu storage in stainless steel tanks. When the electrical conductivity exceeds the normal fluctuation range (Mean + 3 standard deviations) of previous data, the monitoring system recognizes the microbial contamination. By optimizing the conductivity-temperature compensation coefficient and conductivity statistical method, the standard deviation of the method was reduced and the sensitivity of microbial pollution monitoring was improved. The ranges of conductivity and compensation coefficient of common types of Huangjiu were estimated. Interference in conductivity measurements due to environmental factors was minimised through the synchronous comparison of conductivity data for multiple tanks of Huangjiu. The standard deviation (SD), which indicates the fluctuation range of the system, decreased from 143 μS/cm to 2 μS/cm. The monitoring system was then applied in Huangjiu storage tanks with capacities of 60 t and 300 t. Through the comparison of conductivity data change, the abnormal signals caused by microbial contamination during the storage of Huangjiu were found in time. Meanwhile, through off-line detection of total acid of Huangjiu, the effectiveness of microbial contamination on-line detection was verified.

Metal–Organic Framework Based Thermoluminescence Dosimeter

Conventional thermoluminescence (TL) dosimeters, a type of off-line radiation detection device, are made of inorganic materials that usually require high synthesis temperatures (above 600 °C) and exhibit a multiple peak glow curve with an unclear structure–property relationship, posing an urgent need to seek new TL materials. Here, we presented the first case of applying metal–organic frameworks (MOFs) as a new material platform to construct a TL dosimeter. Synthesized at mild conditions (around 100 °C), the MOF-based TL material exhibits a single peak glow curve, high X-ray attenuation efficiency, and excellent dose–response linearity ranging from 0.01 to 10 Gy. The crystalline nature of MOFs associated with unambiguous structural information enables TL mechanism elucidation by theoretical calculations, which indicate that in SCU-300 the stable free radicals responsible for emitting visible light when heated are attributed to the charge transform (CT) from the anionic carboxyl groups to the centrosymmetric benzene ring. Furthermore, micron-sized SCU-300 single crystals were subjected to a point dose monitoring experiment based on a simulated eyeball model, and the measured doses match well (error <4%) with those by Monte Carlo calculations.

Detection of Periodontal Pathogens Based on an Integrated Continuous Flow PCR and Capillary Electrophoresis Microfluidic Chip

Continuous-flow PCR (CF-PCR) can realize rapid DNA amplification because of the high temperature variation rate. However, off-line detection methods for PCR may induce cross contamination. To overcome this problem, we herein fabricated an integrated CF-PCR and electrophoresis microfluidic chip. The optimal voltage applied in the electrophoresis part of the microfluidic chip was achieved by simulation in COMSOL. Coating the inside wall of the microchannel can inhibit electroosmotic flow and improve the resolution for DNA fragments. The temperature distribution of the serpentine part can meet the PCR and has no obvious suppressive effect on sample separation. Finally, we have performed the amplification of target genes for Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola and detected the corresponding PCR products in the microfluidic chip within 11 min. Such work provides a new method for the rapid detection of bacteria.

Wearable Clinic: From Microneedle-Based Sensors to Next-Generation Healthcare Platforms.

The rapid development of wearable biosensing calls for next-generation devices that allow continuous, real-time, and painless monitoring of health status along with responsive medical treatment. Microneedles have exhibited great potential for the direct access of dermal interstitial fluid (ISF) in a minimally invasive manner. Recent studies of microneedle-based devices have evolved from conventional off-line detection to multiplexed, wireless, and integrated sensing. In this review, the classification and fabrication techniques of microneedles are first introduced, and then the representative examples of microneedles for transdermal monitoring with different sensing modalities are summarized. State-of-the-art advances in therapeutic and closed-loop systems are presented to formulate guidelines for the development of next-generation microneedle-based healthcare platforms. The potential challenges and prospects are discussed to pave a new avenue toward pragmatic applications in the real world.

Artificial neural network processed linear-light tristimulus and hue parameters of fluorescence for smartphone assisted point-of-care testing device

Smartphone based point-of-care testing (POCT) device has progressed rapidly for its advantages in simulating the main functions of large instruments. The capability of multiple biomarkers detection and the robust data acquiring and processing ability are significant for POCT device to meet the needs of convenient diagnostics. In this work, a POCT device was developed based on the cascade reaction of targets catalyzed by corresponding oxidases and leaf like zeolitic imidazolate framework. It was found that both linear-light tristimulus (RGB) and hue parameters (HSV) of fluorescence image of cascade reaction product were highly related with target concentration. Furthermore, a smartphone application was constructed to analyze the RGB and HSV of fluorescence to predict target level utilizing artificial neural network (ANN) algorithm. The regression values (R) for the training and validation of four targets were all higher than 95 %. The device realized the off-line detection of targets within 50 min in serum sample, and the assay results were comparable with standard methods. It also performed well in identifying the normal and abnormal serum samples. The proposed platform combining with the smartphone application could be used as a handy and costless POCT device for the rapid monitoring of metabolic biomarkers.

Research on the detection of axle abnormal noise based on maximum autocorrelation kurtosis deconvolution

The axle off-line detection is an important link to ensure the sound quality of the axle. The traditional methods mainly rely on condition indicators, psychoacoustic parameters and artificial intelligence, in which the generation mechanism of the abnormal signal is ignored, thus leading to low accuracy and poor interpretability. To solve these problems, a vehicle axle abnormal sound detection method based on abnormal sound mechanism is proposed in this paper. Firstly, the sound quality of 30 newly produced axles is determined by subjective evaluation, and it is clear that the tooth frequency impulse is the main reason for the abnormal sound of axles. Then, a new objective function, autocorrelation kurtosis, is used to deconvolute the axle vibration signal for the periodic impulse feature. Simulation and experimental results show that the proposed maximum autocorrelation kurtosis deconvolution (MACKD) is more effective than maximum correlation kurtosis deconvolution (MCKD) and minimum entropy deconvolution (MED). On this basis, the impulse autocorrelation kurtosis index (IACK) is constructed and used to quantify the abnormal sound of the axle. The results show that the correlation between the proposed index and the subjective evaluation results is more than 0.9, which can better identify the sound quality of the axle.

Research on Registration Accuracy Detection Method Based on Machine Vision

The online printing machine registration control system can detect the overprint accuracy in real time during the printing process, so as to adjust the corresponding equipment to improve the print quality. The off-line overprint accuracy detection system ensures the stability and reliability of the detection results when detecting the print quality. Under this background, the research and experiment on the algorithm model of overprint accuracy detection of color prints based on machine vision system are carried out. Set up a hardware platform for printing registration accuracy detection with perfect machine vision, complete the collection of registration logo images. Convert the collected graphic sample information into data that can be processed by the computer. The collected image samples can be processed through the corresponding functions of the software platform interface, including graphic segmentation, visual enhancement, filtering and noise reduction, and edge extraction. Finally, the registration accuracy detection is completed through the neural network model for experimental analysis.

An Online Identification Method of Thermal Dissipation State for Forced Air-Cooled System of Power Converters

Thermal stress is the primary cause of malfunction and failure in power modules. As the main heat dissipation component, the heatsink plays a significant role in improving the reliability of power converters. However, due to the continuous accumulation of dust and impurities on the heatsink’s air inlet side, the thermal dissipation performance of the cooling system constantly declines, which influences the reliable operation of power modules. In this article, a computational fluid dynamics (CFD)-based simulation analyzes the declining mechanism of the heatsinks’ thermal transfer performance. Moreover, a novel online method is proposed for the cooling system’s thermal dissipation state identification. This method regards the steady-state thermal resistance as the feature parameter of the thermal dissipation state evaluation. And a parameter identification method is employed to identify the steady-state thermal resistance due to the difficulty of obtaining it in the transient thermal process. In addition, an ac/dc/ac pulsewidth modulation (PWM) converter is built for verification. The experimental results demonstrate that the proposed method enables accurate and fast identification of the cooling systems’ thermal dissipation state. It can be one of a solution for replacing the existing off-line manual periodic detection method, which helps improve detection efficiency and decrease maintenance costs.

A Stator Slot Wedge Loosening Offline Detection System Based on an Intelligent Maintenance Robot of a Large Hydro Generator

Hydropower is at the backbone of low-carbon clean energy, while large hydroelectric generators play a key role in hydropower systems. In this paper, the tightness detection of stator trough wedges in the offline state of large hydroelectric generators is mainly studied. The offline state means that the generator stops running but does not need to move the stator and rotor. The traditional detection of generator stator wedges has problems, such as long maintenance intervals and low work efficiency. According to the structural characteristics of the generator, a generator maintenance robot device based on the track mechanism is designed. The device can simultaneously visualize the internal of the generator and detect the tightness of the stator slot wedge, effectively improving the maintenance efficiency. According to the electromagnetic magnitude of the stator rod in the alternating magnetic field, different stator slot wedge models are built for tightening, slightly tightening and loosening. For the conventional slot wedge loosen detection, there is a problem that the characteristic parameter is single, and the state of the slot wedge cannot be fully fed back. In this paper, a method for extracting the Linear Prediction Cepstrum Coefficient (LPCC) and Mel Frequency Cepstrum Coefficient (MFCC) of percussion sound signal is proposed, and the tightness recognition of the stator slot wedge is realized by combining the BP neural network algorithm. Experimental results show that the proposed method can effectively identify stator slot wedges in different states.

An Efficient Method of Deadlock Detection and Recovery for Flexible Manufacturing Systems by Resource Flow Graphs

Deadlocks are a highly undesirable situation in flexible manufacturing systems (FMSs). This article presents a direct and novel method to detect such markings by constructing the resource flow graph of a Petri net that models an FMS and to recover such markings by adding a set of recovery transitions. First, an algorithm is developed to build a new kind of directed graph called the resource flow graph of a Petri net. Resource flow graphs can well represent the competition for shared resources by different processes. Second, based on the resource flow graph, loop graphs can be found. Furthermore, partial deadlock markings can be easily detected due to their relationship with loop graphs. Then, we propose an algorithm to design a set of recovery transitions for loop graphs that are enabled at partial deadlock markings. The proposed approach can detect partial deadlock markings without generating a complete reachability graph of a Petri net and the resulting net is deadlock-free with all reachable markings by adding the obtained recovery transitions. Finally, some widely used examples are provided to demonstrate the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The occurrence of deadlocks in an FMS tends to cause unnecessary productivity costs and even catastrophic results. In the framework of Petri nets, reachability graph analysis can usually obtain a maximally permissive supervisor of a plant. However, it is rather inefficient since it suffers from the state explosion problem. In this particular research, we develop an off-line deadlock detection and recovery policy by setting a group of virtual events that are not present in a physical model. The proposed approach is computationally efficient since it does not require to generate a reachability graph. It guarantees that the resulted system is deadlock-free with its all original reachable markings.

Research on on-line monitoring data cleaning of transformer oil chromatogram based on machine learning and neural networks

Transformer oil chromatogram on-line monitoring device is a widely used sensing device to monitor the operation state of transformer. Due to the lack of hardware verification means, the on-line chromatographic data generally has the problem of drift. In actual operation, it is mostly used as the basis for trend judgment, but can not reflect the effective value of chromatographic measurement. In this paper, the LSTM neural networks is used to correct the on-line monitoring data of transformer oil chromatogram. The off-line test data combined with neural networks data cleaning technology are used to repeatedly feed back and calibrate the online data, so that the corrected chromatographic on-line number is steadily close to the off-line detection data, which greatly improves the availability and accuracy of on-line chromatographic data. The research results of this paper have important reference significance for the data cleaning of on-line monitoring and measurement of electrical equipment.

Off-line ESI-MS/MS profiling of betalains and flavonoid glycosides isolated from (fruit) Opuntia stricta var. dillenii and (vegetable) Atriplex hortensis var. rubra by countercurrent chromatography

Opuntia stricta var. dillenii and Atriplex hortensis var. rubra, two plant species with abundant betalain and flavonoid content, were studied by high performance countercurrent chromatography (HPCCC) coupled with ESI-MS/MS in an off-line detection process. Both CCC runs used tert.-butyl-methyl-ether/n-butanol/acetonitrile/water 1% trifluoroacetic acid (v/v/v/v, 2:2:1:5) as a biphasic solvent system within an elution-extrusion mode. The resulting fractions were scanned by an off-line sequential injection to ESI-MS/MS approach. The separation of Opuntia stricta var. dillenii extract showed a partial isolation of principal betalains (betanin) from their derivatives (betanidin, phyllocactin, feruloyl-betanin, betaxanthin), degraded products (hydroxyl-neobetanin, mono decarboxy-betanins), and flavonoid glycosides (isorhamnetin-rutinoside, kaempferol-rutinoside). In case of Atriplex hortensis var. rubra, this study confirmed the coexistence of several flavonoids (quercetin-O-malonyl glucoside, kaempferol-O-malonyl glucoside) and generated few pure pigment fractions (celosianin II) for structural elucidation.

Intelligent wearable system with accurate detection of abnormal gait and timely cueing for mobility enhancement of people with Parkinson's disease.

Previously reported wearable systems for people with Parkinson's disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.

Rapid Offline Detection and 3d Annotation of Assembly Elements in the Augmented Assembly

Rapid Offline Detection and 3d Annotation of Assembly Elements in the Augmented Assembly

Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.

Off-line fault detection of logical control networks

This paper investigates the off-line fault detectability of logical control networks (LCNs) by the method of semi-tensor product. Firstly, two concepts of off-line fault detectability, that is, weak off-line fault detectability and strong off-line detectability, are presented. Secondly, based on a recursive algorithm, the verification matrix for off-line fault detection is proposed to verify the off-line detectability of LCNs. Thirdly, necessary and sufficient conditions are presented to analyse the off-line fault detectability of LCNs. Finally, two illustrative examples show the effectiveness of the obtained new results.

Research and Application of Electromagnetic Wave Signal Acquisition Device for Power System Circuit Breaker

Power circuit breaker is an important tool to satisfy the controllability and safety of power system. Therefore, to ensure the stable operation of the circuit breaker is to ensure the normal supply of electric energy. In order to better capture the electromagnetic wave signal radiated by the circuit breaker and better judge the state of the electromagnetic wave radiated by the arc of the circuit breaker, a new type of vibration trigger device specially used for collecting the electromagnetic wave signal radiated by the circuit breaker and its use method are proposed in this paper. This method can collect the electromagnetic wave signal of circuit breaker through the optimized design of vibration trigger device. The device uses all metal integrated cast aluminum shell and independent power supply. This device can effectively resist electromagnetic interference, and is convenient for the acquisition of electromagnetic wave signal. The device developed in this paper and its application method belong to the field of off-line detection of electrical equipment, and are especially suitable for vibration triggering of the device when collecting the radiated electromagnetic wave signal during the action of circuit breaker.