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While research has shown that wearing a disguise hinders lineup identifications, less is known about how to conduct lineups in cases of disguised perpetrators. We examined the influence of surgical masks, worn during a crime event (encoding) and within lineups (retrieval), on eyewitness identification accuracy. In our experiment, 452 participants watched a mock-crime video and identified the perpetrator from either a target-present or a target-absent simultaneous lineup. Contrary to expectations based on the encoding specificity principle, we did not find that matching the presence of masks in the lineup to the encoding condition increased identification accuracy. Instead, compared to the condition with no masks at encoding and retrieval, the presence of masks at either stage negatively affected discriminability and undermined the predictive utility of confidence and decision time. Our findings indicate that when a witness has encountered a masked perpetrator, presenting them with a masked lineup may not be necessary. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a novel approach to personal risk (PR) identification using federated learning (FL) in wireless communication scenarios, leveraging generalized information. The primary focus is on harnessing the power of distributed data across various wireless devices while ensuring data privacy and security, a critical concern in PR assessment. To this end, we propose an FL-based model that effectively aggregates learning from diverse, decentralized data sources to analyze the PR factors. The proposed method involves training local models on individual devices, which are then aggregated to form a comprehensive global model. This process not only preserves data privacy by keeping sensitive information on the device but also utilizes the widespread availability and connectivity of wireless devices to enhance data richness and model robustness. To address the challenges posed by the wireless environment, such as data heterogeneity and communication constraints, we further implement advanced aggregation algorithms and optimization techniques tailored to these unique conditions. We finally evaluate the performance of our proposed method based on two primary metrics of identification accuracy and convergence rate of the federated learning process. Through extensive simulations and real-world experiments, we demonstrate that our approach not only achieves high accuracy in PR identification but also ensures rapid convergence, making it a viable solution for real-time risk assessment in wireless networks.

This paper introduces a novel approach utilizing normalized flow networks (NFNs) for dynamic personal risk (PR) analysis, specifically focusing on the assessment of two-way data rates at network nodes. NFNs, a sophisticated paradigm in data processing and modeling derived from machine learning principles, serve as the foundational framework for our analysis. Leveraging NFNs, we develop a generalized method that integrates information transmission techniques into PR dynamics, enabling a comprehensive examination of communication efficacy within network structures. Our study entails the formulation of dynamic models tailored to capture the evolving nature of PR interactions, facilitating the evaluation of data rates exchanged between network nodes. Through extensive simulations and empirical validation, we demonstrate the effectiveness of our approach in elucidating the intricate dynamics of PR campaigns and quantifying the impact on the network performance. The findings underscore the significance of leveraging NFNs for dynamic PR analysis, offering valuable insights into optimizing communication strategies and enhancing network efficiency in diverse domains.

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Improved patient-ventilator asynchrony (PVA) identification using waveform analysis by critical care physicians (CCPs) may improve patient outcomes. This study aimed to assess the ability of CCPs to identify different types of PVAs using waveform analysis as well as factors related to this ability. METHODS: We surveyed 12 university-affiliated medical ICUs (MICUs) in Tunisia. CCPs practicing in these MICUs were asked to visually identify 4 clinical cases, each corresponding to a different PVA. We collected the following characteristics regarding CCPs: scientific grade, years of experience, prior training in mechanical ventilation, prior exposure to waveform analysis, and the characteristics of the MICUs in which they practice. Respondents were categorized into 2 groups based on their ability to correctly identify PVAs (defined as the correct identification of at least 3 of the 4 PVA cases). Univariate analysis was performed to identify factors related to the correct identification of PVA. RESULTS: Among 136 included CCPs, 72 (52.9%) responded to the present survey. The respondents comprised 59 (81.9%) residents, and 13 (18.1%) senior physicians. Further, 50 (69.4%) respondents had attended prior training in mechanical ventilation. Moreover, 21 (29.2%) of the respondents could correctly identify PVAs. Double-triggering was the most frequently identified PVA type, 43 (59.7%), followed by auto-triggering, 36 (50%); premature cycling, 28 (38.9%); and ineffective efforts, 25 (34.7%). Univariate analysis indicated that senior physicians had a better ability to correctly identify PVAs than residents (7 [53.8%] vs 14 [23.7%], P = .044). CONCLUSIONS: The present study revealed a significant deficiency in the accurate visual identification of PVAs among CCPs in the MICUs. When compared to residents, senior physicians exhibited a notably superior aptitude for correctly recognizing PVAs. [ABSTRACT FROM AUTHOR]

In the present study, 140 participants were instructed to use absolute judgement or relative judgement just prior to facing a present perpetrator lineup or an absent perpetrator lineup. Participants’ eye movements were recorded during the lineup presentation to assess whether they were able to apply the instructions they had been given. The results showed no significant differences between the type of instruction and the identification accuracy in the lineups. However, eye-tracking data showed that participants who were given absolute judgement instructions made significantly fewer interphotograph comparisons than those who received relative judgement instructions on both types of lineups. Additionally, in the present perpetrator lineup, participants spent less time looking at the set of photographs of the lineup in the absolute judgement condition than in the relative judgement condition. Moreover, participants’ meta-memory evaluations indicated a certain degree of correspondence between participants’ self-reports and their eye-tracking data. Finally, a weak relationship was observed between post-lineup confidence and accuracy on both lineups. Therefore, although eye movement data showed that participants in the absolute judgement condition could partially implement the instructions, this did not translate into a higher accuracy rate in any of the lineups. However, it should be noted that in the present perpetrator lineup, when participants made fewer interphotograph comparisons (a performance more characteristic of an absolute judgement) they were more accurate in the identification lineup.

Distance, lighting, and facial masking negatively impact eyewitness identification accuracy. We investigated their combined effect on accuracy and how internal (e.g., eyes) versus external (e.g., hair) masking impacts accuracy. Using live targets witnessed by 1325 participants, we investigated the effects of distance (5m, 12.5m, 20m), lighting (optimal:300lx, suboptimal:2lx), facial masking (no facial masking, sunglasses, hood, sunglasses & hood), and eyewitness age (5-90) on identification accuracy in target-present (TP) or target-absent (TA) eight-person simultaneous photograph line-ups. TP identification accuracy, with no facial masking, for all participants was .69 (.96 for only 18-44-year-old choosers) at 5m, .34 (.58) at 12.5m, and .17 (.42) at 20m. TA rejection accuracy for all participants was .63 (.60 for only 18-44-year-olds) at 5m, .42 (.54) at 12.5m, and .46 (.46) at 20m. Facial masking further decreased accuracy; internal facial masking had the strongest negative effect. The combined negative effects of distance, lighting, and facial masking resulted in chance-level performance in TP line-ups (i.e., .125) in some instances. We also found a positive association between accuracy and high confidence and shorter response times. We recommend that law enforcement agencies and researchers report these variables to allow for the postdiction of the likely accuracy of an eyewitness decision. [ABSTRACT FROM AUTHOR]

The identification of apple leaf diseases is crucial to reduce yield reduction and timely take disease control measures. Employing deep learning for apple leaf disease identification is challenging because of the limited availability of samples for supervised training and the serious class imbalance. Hence, this paper proposes an accurate deep learning-based pipeline to solve the problem of limited data sets on farms and reduce the partiality due to serious class imbalance. Firstly, an improved cycle-consistent adversarial networks (CycleGAN) is used to generate synthetic samples to improve the learning of data distribution and solve the problems of small data sets and class imbalance. Secondly, ResNet is trained as a baseline convolutional neural network classifier to classify apple leaf diseases. The experimental results show that ResNet has the highest recognition accuracy on the test set, reaching 97.78%, and the classification accuracy is significantly improved by the generated synthetic samples (+ 14.7%). In addition, the experiment results of t-distributed stochastic neighbor embedding (t-SNE) and visual Turing test visually confirmed that the images generated by improved CycleGAN have much better quality and are more convincing. [ABSTRACT FROM AUTHOR]

Specific emitter identification (SEI) is a powerful technique that identifies different emitters utilizing the features generated from hardware imperfections. The features, also known as radio frequency fingerprints (RFFs), can be extracted via model-based methods or data-based methods. In this paper, we analyze the identification accuracy of SEI system and provide criteria for evaluating different SEI schemes. We use in-phase/quadrature (I/Q) imbalance as an example to examine the identification performance by analyzing the Euclidean distance between radio frequency (RF) signals of different emitters. This derivation can also be generalized to different modulation schemes and distortion models.

In order to achieve continuous and dynamic detection of sugarcane nodes, improve the automatic production efficiency of pre-cut sugarcane seed, and lower the cost of mechanized sugarcane production, a detection method based on linear array charge-coupled device (CCD) photoelectric sensor signal processing was developed. Firstly, the mechanical drive unit was controlled to drive the photoelectric detection system to acquire the signal of the vertical projection of the sugarcane profile. The projection information was then binarized into profile information using the Otsu algorithm. The profile signal was then decomposed using a variable mode decomposition algorithm optimized based on the sparrow search algorithm, and the component reflecting the node content was regarded as the feature signal. Finally, the position of the wave peaks above the judgment threshold in the normalized feature signal was considered the position of the sugarcane nodes. One-way and two-way experiments were conducted to investigate the effects of scan speed and illuminance on identification precision. The results showed that the identification rate, average response time, and average error values were 98.40%, 0.13 s, and 1.36 mm at a scan speed of 75 mm/s and an illuminance of 91.91 lx. Compared to other node identification methods discussed in this article, the proposed method has a high identification rate and accuracy with a high response speed, which can improve the automation efficiency of sugarcane seed production. [ABSTRACT FROM AUTHOR]

Mask wearing is the easiest and most effective way to avoid COVID-19 infection; however, it affects interpersonal activities, especially face identification. This study examined the effects of three mask coverage levels (full coverage, FC; coverage up to the middle [MB] or bottom of the nose bridge [BB]) on face identification accuracy and time. A total of 115 university students (60 men and 55 women) were recruited to conduct a computer-based simulation test consisting of 30 questions (10 questions [five face images each of men and women] for the three mask coverage levels). One unmasked target face and four face images with a specified mask coverage level were designed for each question, and the participants were requested to select the same face from the four covered face images on the basis of the target face. The ANOVA results indicated that identification accuracy was significantly affected by sex (p < 0.01) and the mask coverage level (p < 0.001), whereas identification time was only influenced by sex (p < 0.05). The multiple comparison results indicated that the identification accuracy rate for faces wearing a mask with FC (90.3%) was significantly lower than for those wearing masks with coverage up to the MB (93.7%) and BB (94.9%) positions; however, no difference in identification accuracy rate was observed between the MB and BB levels. Women exhibited a higher identification accuracy rate than men (94.1% vs. 91.9%) in identifying unfamiliar faces, even though they may spend less time identifying the images. A smaller mask coverage level (i.e., the BB level) does not facilitate face identification. The findings can be served as a reference for people to trade-off between wearing a mask and interpersonal interaction in their daily activities. [ABSTRACT FROM AUTHOR]

Rice diseases are one of the key factors affecting the yield and quality of rice and their identification is critical. However, taking rice disease images in the field is time-consuming and laborious. Furthermore, the images taken in the rice field contain complex background information, such as weeds, soil, and unwanted sections of rice plants. The use of only limited annotated images to rapidly and accurately identify diseases in complex scenes is key problem to be solved. A two-stage method called RiceNet was proposed to identify important four rice diseases, including rice panicle neck blast, rice false smut, rice leaf blast, and rice stem blast. In the first instance, YoloX was used to detect the diseased parts of rice, and the original rice disease images were clipped to form a new rice disease patch dataset according to the detection results. In the second stage, Siamese Network was used to identify the rice disease patch dataset obtained in the first stage. For the detection stage, the mAP of YoloX for rice disease images was 95.58%, and the comparison experiment showed that YoloX achieved the highest detection performance. For the identification stage, Siamese Network achieved the identification accuracy of 99.03%, which is higher than that of other models. The experimental results show that the proposed RiceNet model was superior to state-of-the-art methods. Moreover, it achieved a high detection speed with the smallest weight size for the identification of rice diseases. [Display omitted] • Accurate location of limited annotated rice disease images in complex scenes. • Effective and efficient four rice disease location and identification automatically. • Two-stage method combining object detection and Few-shot Learning is proposed. • Proposed model achieves a mAP of 95.58% and identification accuracy of 99.03%. [ABSTRACT FROM AUTHOR]

Although peer review has been widely used for formative assessment in writing instruction, there remain concerns about whether assessors are at a sufficient writing performance level that would allow them to identify major problems in the reviewed work and provide helpful feedback to improve draft quality. Little empirical research has examined how assessor writing performance specifically influences problem identification accuracy and helpfulness of feedback, nor has it acknowledged different grain sizes of assessor performance. Assessor writing performance at different grain sizes (i.e., performance at the levels of genre, dimension of a genre, and specific problem topic) was assessed alongside problem identification accuracy and feedback helpfulness in 234 high school students who participated in an anonymous multipeer review in a secondary writing course in the United States. A correlation analysis showed that assessor performance levels on specific problem topics were meaningfully separable, thereby allowing for consideration of the effects of assessor performance at genre, dimension, and topic levels. Multiple regression results indicated that assessor writing performance was unrelated to problem identification accuracy at any grain size. Therefore, scaffolds in the reviewing process appear sufficient to support problem identification accuracy. However, assessor writing performance, particularly on specific dimensions and specific topics, consistently predicted helpfulness of feedback, even though lower performing assessors rarely produce incorrect advice. Theoretical and practical implications of the findings are discussed. Educational Impact and Implications Statement: Assessor writing performance is conceptualized in terms of different grain sizes: overall writing performance, genre writing performance, writing performance on a cluster of dimensions, dimensional writing performance, and topic writing performance. An assessor who is at a given writing performance level at a large grain size may often be at varying performance levels at more fine-grain sizes. Assessors at both high and low writing performance levels, at any grain size, are equally likely to identify problems accurately. Assessors with high performance on specific dimensions and topics are more likely to provide helpful feedback on those dimensions and topics. [ABSTRACT FROM AUTHOR]

Eyewitness identification is a procedural act that is influenced by various psychological factors. Scientific research has demonstrated that the way identification procedures are conducted and administered affects witnesses' identification decisions and their confidence in those decisions. Research into these variables has also led to best-practice guidelines for conducting eyewitness identification. However, the legal system in Estonia, as have those in many other places, has been slow to adopt the recommendations and has adhered to traditional principles instead, which is reflected in the law on eyewitness identification. This article analyses whether Estonia's law governing eyewitness identification is consistent with evidence-based recommendations. It first presents an overview of variables related to the reliability of identification evidence over which the criminal-justice system has control, and then compares the most important findings from scientific literature (and the resulting best practices) with the current law. Finally, it highlights specific areas of law wherein adjustments could produce better alignment with the findings from scientific research. The authors conclude that the law today, leaving many decisions up to law-enforcement entities, displays a need for additional official guidelines. The article highlights the importance of using scientific research to inform legal practices. [ABSTRACT FROM AUTHOR]

Endocrine disrupting chemicals are of concern because of possible human health effects, thus they are frequently included in biomonitoring studies. Current analytical methods are focused on known chemicals and are incapable of identifying or quantifying other unknown chemicals and their metabolites. Non-targeted analysis (NTA) methods are advantageous since they allow for broad chemical screening, which provides a more comprehensive characterization of human chemical exposure, and can allow elucidation of metabolic pathways for unknown chemicals. There are still many challenges associated with NTA, which can impact the results obtained. The chemical space, i.e., the group of known and possible compounds within the scope of the method, must clearly be defined based on the sample preparation, as this is critical in identifying chemicals with confidence. Data acquisition modes and mobile phase additives used with liquid chromatography coupled to high-resolution mass-spectrometry can affect the chemicals ionized and structural identification based on the spectral quality. In this study, a sample preparation method was developed using a novel clean-up approach with CarbonS cartridges, for endocrine-disrupting chemicals in urine, including new bisphenol A analogues and benzophenone-based UV filters, like methyl bis (4-hydroxyphenyl acetate). The study showed that data dependent acquisition (DDA) had a lower identification rate (40%) at low spiking levels, i.e., 1 ng/mL, compared to data independent acquisition (DIA) (57%), when Compound Discoverer was used. In DDA, more compounds were identified using Compound Discoverer, with an identification rate of 95% when ammonium acetate was compared to acetic acid (82%) as a mobile phase additive. TraceFinder software had an identification rate of 53% at 1 ng/mL spiking level using the DDA data, compared to 40% using the DIA data. Using the developed method, 2,4 bisphenol F was identified for the first time in urine samples. The results show how NTA can provide human exposure information for risk assessment and regulatory action but standardized reporting of procedures is needed to ensure study results are reproducible and accurate. His Majesty the King in Right of Canada, as represented by the Minister of Health, 2024 [Display omitted] • A robust non-targeted analysis (NTA) method was developed to screen for endocrine disruptors in urine. • Influence of data acquisition modes on identification rate was investigated. • Influence of LC mobile phase additives on data acquisition and identification rate was investigated. • Influence of sample preparation on non-targeted analysis chemical space was studied. • Data acquisition and LC mobile phase additives in NTA need to be standardized to provide reliable and reproducible data. [ABSTRACT FROM AUTHOR]

The problem of identifying the parameters of a linear object in the presence of non-Gaussian noise is considered. The identification algorithm is a gradient procedure for maximizing the functional, which is a correntropy. This functionality allows you to get estimates that have robust properties. In contrast to the commonly used Gaussian kernels, the centers of which are at zero and effective for distributions with zero mean, the paper considers a modification of the criterion suitable for distributions with nonzero mean. The modification is to use correntropy with a variable center The use of Gaussian kernels with a variable center will allow us to estimate unknown parameters under Gaussian and non-Gaussian noises with zero and non-zero mean distributions and provide an opportunity to develop new technologies for data analysis and processing. It is important to develop a robust identification algorithm based on correntropy with variable center. Their properties in the identification of stationary and non-stationary objects are the subject of research. The goal is to develop a robust identification algorithm that maximizes the criterion of correntropy with a variable center using center configuration procedures and kernel width and to study its convergence in stationary and non-stationary cases under non-Gaussian noise. Expressions for steady-state value of the estimation error are obtained, which depend on the type of noise distribution and the degree of non-stationarity of the estimated parameters The following tasks are solved: to investigate the convergence of the algorithm and determine the conditions for the stability of the established identification process. Methods of estimation theory (identification) and probability theory are used. The following results were obtained: 1) the developed algorithm provides robust estimates in the presence of noises having a distribution with zero and non-zero mean; 2) its convergence was studied in stationary and non-stationary cases under conditions of Gaussian and non-Gaussian noise; 3) simulation of the algorithm was carried out. 1) the developed algorithm consists in the development of a robust identification algorithm that maximizes the criterion of correntropy with a variable center; 2) its convergence in stationary and non-stationary cases in the conditions of Gaussian and non-Gaussian noises is investigated; 3) simulation of the algorithm is performed. Conclusions: The results of the current study will improve existing data processing technologies based on robust estimates and accelerate the development of new computing programs in real time.

Damage detection of structures based on swarm intelligence optimization algorithms is an effective method for structural damage detection and key parts of the field of structural health monitoring. Based on the chimp optimization algorithm (ChOA) and the whale optimization algorithm, this paper proposes a novel hybrid whale-chimp optimization algorithm (W-ChOA) for structural damage detection. To improve the identification accuracy of the ChOA, the Sobol sequence is adopted in the population initialization stage to make the population evenly fill the entire solution space. In addition, to improve the local search ability of the traditional ChOA, the bubble-net hunting mechanism and the random search mechanism of the whale optimization algorithm are introduced into the position update process of the ChOA. In this paper, the validity and applicability of the proposed method are illustrated by a two-story rigid frame model and a simply supported beam model. Simulations show that the presented method has much better performance than the ChOA, especially in dealing with multiple damage detection cases. The W-ChOA has good performance in both overcoming misjudgment and improving computational efficiency, which should be a preferred choice in adoption for structural damage detection. [ABSTRACT FROM AUTHOR]

Maximum Entropy model (MaxEnt), as a machine learning algorithm, is widely used to identify potential risk areas for emerging infectious diseases. However, MaxEnt usually overlooks the influence of the optimal selection of spatial grid scale and the optimal combination of factor information on identification accuracy. Furthermore, the internal level information of factors is closely related to the potential risk of disease occurrence but is rarely applied to enhance MaxEnt's accuracy. In this study, the Optimal Parameters-based Geographical Detectors-Information Value-MaxEnt (OPGD-IV-MaxEnt) was first proposed to identify the potential risk areas of hand, foot, and mouth disease (HFMD) in Shenzhen and compared its identification accuracy with that of OPGD-MaxEnt and MaxEnt. Firstly, the optimal grid scale and optimal combination of factor information were determined by OPGD. Secondly, the contributions of factors' internal level information to the potential risk of HFMD occurrence were quantified and incorporated by IV. Lastly, the spatial patterns of potential risk areas and their main driving factors were elucidated. Results showed that: (i) Area under the curve (AUC) of single MaxEnt were 0.638, 0.688, 0.763, 0.796, and 0.757 at 100 m, 250 m, 500 m, 750 m, and 1000 m scale, respectively, and 750 m were deemed the optimal scale. (ii) At the optimal scale, OPGD-IV-MaxEnt (AUC = 0.868) identified potential risk areas more accurately than MaxEnt (AUC = 0.796) and OPGD-MaxEnt (AUC = 0.827). (iii) Resident (r = 0.61, q = 0.39) and Market (r = 0.61, q = 0.36) were the primary factors affecting the identification of potential risk areas. (iv) Potential high-risk areas of HFMD were mainly distributed in northwestern, southwestern, and central Shenzhen, with dense resident and market distribution. Such insights are instrumental in devising targeted infection prevention and control measures for emerging infectious diseases and provide references for improving the identification accuracy of similar machine learning algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to achieve good identification performance, the existing identification method of crop diseases based on deep learning needs considerable annotated images to train. However, collection of crop leaves disease images in field is time-consuming and laborious, so it is urgent to propose a timely and effective identification model for limited labeled crop disease images. This paper proposed a Few-shot Learning method based on Siamese Network to identify crop leaves diseases, which used randomly generated image sample pairs as input. In experiments, since crops are divided into monocotyledons and dicotyledons, this paper used Few-shot Learning method to train monocotyledon plant diseases model named SiamNet2 and dicotyledon plant diseases model named SiamNet1, which were used to identify monocotyledon and dicotyledon plant diseases. The results of 5-way 5-shot dicotyledon plant disease identification and 10-way 10-shot monocotyledon plant disease identification showed that the identification accuracy of SiamNet2 was 8.7% and 12.4% higher than that of SiamNet1, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification. [ABSTRACT FROM AUTHOR]

Citizen science initiatives contain a large volume of observations that can be useful to address ecological questions for a wide array of organisms. However, one limitation of citizen science data is the potential for species misidentification. Although recent studies have shown that citizen science data are relatively accurate for many taxa, the effect of misidentification errors in hard‐to‐identify species remains poorly explored. If misidentification events occur at large scales, ecological estimates can be compromised. Here, we show that misidentifications contained in citizen science databases biased phenological estimates in a pair of migratory and partially overlapping Neotropical flycatchers: the Chilean Elaenia Elaenia chilensis and the Small‐billed Elaenia Elaenia parvirostris. We reviewed and re‐classified 4399 photos of these species from Argentina, Chile and Uruguay. We found that overall identification accuracy was high (c. 90%) for both species when they were allopatric, but dramatically low for Small‐billed Elaenia during autumn migration (from 28.6% to 84.6%) because migrating individuals of Chilean Elaenia were systematically reported as Small‐billed. The phenological estimates for both Elaenias were biased due to the large number of misidentifications concentrated towards the autumn migration period. These errors caused a 1‐week advancement in the estimated arrival, and a 2‐week delay in the estimated departure for Small‐billed Elaenia. For Chilean Elaenia, errors caused a 1‐week delay for the estimated spring peak passage and underestimation of the magnitude of the autumn passage. Our results highlight the importance of performing critical assessments of records when using citizen science databases to describe ecological patterns in species that are hard to identify. The large volume of information provided by citizen science initiatives is useful to describe spatio‐temporal patterns in birds, particularly in those of poorly known regions. However, to further enhance the usefulness of such databases, it is imperative actively to post‐process and contrast patterns derived from documented (and undocumented) records, with a special focus on misidentifications. This will only be possible through a thorough review of the documented data, together with an intimate understanding of the natural history of the study species. [ABSTRACT FROM AUTHOR]

Incompliance of the settings of the system to control actual values of the parameters of a variable frequency induction electric drive may sometimes result in complete non-operability of a variable frequency electric drive as well as in the considerable reduction of the dynamic quality parameters. Such parameters as active rotor resistance, rotor inductance, and inductance of the magnetization circuit are available for the immediate measuring. They are not identified in terms of the acceptance tests, and the values presented in catalogues and reference books are calculated ones that may differ considerably from the real values of a certain machine. Despite constant studies by the researchers, a task to identify electromagnetic parameters of the equivalent circuit of an induction motor is still important and topical. The objective of the paper is to develop a method of online-identification of the electromagnetic parameters of an induction motor making it possible to implement accurate regulator adjustment of the frequency control system in terms of operational changes in the driving motor parameters. For the first time, the paper analyzes a steady mode of induction motor operation which does not apply T-network of the equivalent circuit of an induction motor. An approach has been proposed relying on the equation of an induction motor in three-phase fixed coordinate system obtained on the basis of the theory of generalized electromechanical converter.

Damage detection of structures based on swarm intelligence optimization algorithms is an effective method for structural damage detection and key parts of the field of structural health monitoring. Based on the chimp optimization algorithm (ChOA) and the whale optimization algorithm, this paper proposes a novel hybrid whale-chimp optimization algorithm (W-ChOA) for structural damage detection. To improve the identification accuracy of the ChOA, the Sobol sequence is adopted in the population initialization stage to make the population evenly fill the entire solution space. In addition, to improve the local search ability of the traditional ChOA, the bubble-net hunting mechanism and the random search mechanism of the whale optimization algorithm are introduced into the position update process of the ChOA. In this paper, the validity and applicability of the proposed method are illustrated by a two-story rigid frame model and a simply supported beam model. Simulations show that the presented method has much better performance than the ChOA, especially in dealing with multiple damage detection cases. The W-ChOA has good performance in both overcoming misjudgment and improving computational efficiency, which should be a preferred choice in adoption for structural damage detection.

Learning style theories have been widely used in adaptive learning systems to enhance learning outcomes. However, the previous studies on adaptive learning systems set a high entry barrier for researchers who lack programming skills, and few of the studies involved authentic everyday learning materials. This author proposes to test the feasibility of eye-tracking technology in identifying learning styles with everyday materials, as well as the identification accuracy. This author selected the Felder-Silverman's learning style model (FSLSM) as the framework, enlisted the behaviour patterns that can be used to identify the eight learning styles in the FSLSM model, and conducted a quasi-experiment to test whether these behaviour patterns apply to eye movement differences. Then, this author compared the results of eye-tracking identification with participants' self-report based on Index of Learning Style (ILS) questionnaire for identification accuracy. This quasi-experiment recruited 30 university students, including 19 female and 11 male. Findings showed that eye-tracking technology has the potential to quickly identify learners of different types categorised by the FSLSM theory, with accuracy ranging from 63.50% to 84.67%; however, there are disturbing factors contributing to different levels of identification accuracy, which should be investigated in future research. [ABSTRACT FROM AUTHOR]

• First conduct loose particle detection research based on overlapping signals. • First combine the signal detection research with the material identification research. • Newly propose the definition of confidence coefficient. • Newly propose the definition of two types of identification accuracy. • Verify the proposed method in the latest real application scenarios. Loose particle is an important factor affecting the reliability of aerospace relays. Particle Impact Noise Detection method is a commonly used loose particle detection method, but it can generate interference signals. Accurately identifying loose particle signals and component signals becomes the key to accurately detecting loose particles. Meanwhile, deeply exploring the properties of loose particle signals and further provide material information can provide guidance for manufacturing processes. However, the existing loose particle signal and component signal identification research, as well as the loose particle material identification research, has problems such as limited research objects to pure signals, independent identification results, and failure to refer to the detection requirements in real application scenarios. It is difficult to apply and its practicality is not high. Based on this, the authors proposed a signal detection and material identification method for loose particles based on overlapping signals. Specifically, referring to the latest loose particle detection information, aerospace relay samples were made, and the component identification model and material identification model based on parameter-optimized random forest were trained, respectively. They can achieve good classification effects on data sets created from pure loose particle signals, pure component signals, and pure loose particle signals corresponding to different materials, respectively. On this basis, confidence coefficient was proposed to quantify the degradation degree of the classification effects of the two models on the data set created from overlapping signals, thus the component confidence coefficient and the loose particle confidence coefficient were obtained, respectively. They can be used to determine valid pulses from pure loose particle signals, pure component signals, and mixed signals in overlapping signals, completing loose particle detection. Valid pulses from pure loose particle signals were screened for material identification. In this way, for the aerospace relay to be tested, first, the loose particle detection results can be obtained by a comprehensive judgment of the component identification model, component confidence coefficient, and loose particle confidence coefficient. Second, the material identification results can be obtained by combining the material identification model with the majority voting processing. In addition, the definition of identification accuracy applicable to the loose particle detection was proposed to meet the engineering application requirements in real application scenarios. Multiple experiments verified the feasibility, practicality, and stability of the proposed method. [ABSTRACT FROM AUTHOR]

The rapid and accurate identification of water inrush source plays an important role in preventing and controlling mine water inrush disaster. According to the difference in composition and content of the chemical component of groundwater in different aquifers in the mine, the 9 water chemical components of total hardness, Ph value, Na++K+, Ca2+, Mg2+, Cl-, SO4-, HCO3-, and mineralization are selected as the identification indicators of water source type. On this basis, combined with the hydrogeological data of typical coal mines, the principal component analysis (PCA), K-dimension tree (KD), and K-nearest neighbor (KNN) algorithm were used to establish a water source identification model for mine water inrush and compared with the traditional water source identification model. The research results show that the water source identification model based on PCA-KD-KNN reduces the complexity of calculation, overcomes the influence of information overlap between indicators on the identification results, and effectively improves the identification accuracy. The research can provide a certain basis and reference for the identification of water inrush sources in mines. [ABSTRACT FROM AUTHOR]

In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.

In this study, the influence of the static component in the total force and the effective frequency bandwidth on the accuracy of force identification has been investigated. The acceleration and bending moment responses at different locations of a simply supported beam under different moving forces are numerically measured. The fast Fourier transformation is also introduced to analyze the frequency-domain component of the dynamic responses of the beam. Simulation results show that the dynamic characteristics of the vehicle, such as the frequency of dynamic vehicle load, have significant effect on the proportion of static component in the total vehicle load; the higher the proportion of static component in the total force, the higher the identification accuracy. In addition, the wider the effective frequency bandwidth, the higher the identification accuracy. The numerical results also show that both the proportion of static component in the total force and the effective frequency bandwidth vary with the type and location of measurement. To more accurately identify the moving force, it is necessary to analyze first the static component and frequency characteristics of the measured responses and to select appropriate type and location of measurement. [ABSTRACT FROM AUTHOR]

Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing mechanized systems to remove the tedium and bias associated with manual checking. Here the proposed system performs identification with nearest neighbor matching between offline signature images which are collected temporally. The raw images and their extracted features are preserved using case-based reasoning and feature engineering principles. Image patterns are captured through standard global and local features, along with some profitable indigenously developed features. Outlier feature values, on detection, are automatically replaced by their nearest statistically determined limit values. Search space reduction possibilities within the case base are probed on a few selected key features, applying hierarchical clustering and dendrogram representation. Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well-known approaches. [ABSTRACT FROM AUTHOR]

Load modelling is significant to ensure the accuracy of power system simulation. In previous research on load modelling, various optimisation algorithms have been widely applied. However, the achievement of the global optimal solution depends on the quality of the initial feasible solutions (IFSs). In this study, an enhanced measurement‐based load modelling approach with ensemble learning‐based initialisation is proposed to solve this problem. In the proposed method, an ensemble intelligent machine (EIM) is trained offline to provide high‐quality IFSs based on which the load model parameters can be identified through optimisation. The input features of the EIM are extracted through numerical subspace state‐space system identification from the measurement data, while the output of the EIM is the estimated load model parameters. Then, based on the offline generated samples, a group of individual intelligent units (IIUs) is trained and selected first, after which they are integrated to form an EIM. The enhanced load modelling approach is tested in a simulation case for the Guangdong power grid. The results show that the EIM has better performance than all the IIUs, and the identification accuracy of the load model parameters can be improved with the EIM estimated parameters as the IFSs. [ABSTRACT FROM AUTHOR]

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.

Electric shock current identification is essential for the safety in power distribution network. Moreover, as different categories of object have different electric shock current characteristic, a classification model for shock current is essential to be proposed before identification. Therefore, the authors proposed a two-stage framework, including the AdaBoost for the classification and an improved support vector machine (SVM) method for the identification. In the classification stage, the AdaBoost learns the hidden pattern of different electric shock current and generates a predictive model for current classification. Based on the classification results, a fusion method called SVM–NN is proposed in the identification stage, which is based on SVM and neural network (NN) to make fusion determination. The SVM–NN takes advantages of SVM and NN for integration analysis. Based on real data, these classification and identification methods are evaluated. Results show that the proposed method can significantly improve the identification accuracy of electric shock current signal comparing to traditional methods.

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters. [ABSTRACT FROM AUTHOR]

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.

The undergraduates majored in mechanical engineering are quite good at analyzing an engineering system from the sides of space, mechanism and motion. However, the main course, Basic Control of Mechanical and Electrical Systems, focuses on its model. The essential experiment of this course, the identification of transfer function, offers solution. In this work, the authors describe the identification method of both transfer function and Bode diagram using sweep frequency method. All experiments were carried out using a self-developed experiment device which is hardware configurable, software programmable, content expandable and multi-parameter adjustable. It is noted that there are three key technologies that affect the identification accuracy, being the calibration of voltage input and speed, the signal storage and clock accuracy acquisition. In addition, C# programmed key codes and annotations are provided by the authors. Through this experiment, the internal relationship between Bode diagram and electromechanical parameters is revealed in this work. [ABSTRACT FROM AUTHOR]

An electronic-nose is developed based on eight quartz-crystal-microbalance (QCM) gas sensors in a sensor box, and is used to detect Chinese liquors at room temperature. Each sensor is a highly-accurate and highly-sensitive oscillator that has experienced airflow disturbances under the condition of varying room temperatures due to unstable flow-induced forces on the sensors surfaces. The three-dimensional (3D) nature of the airflow inside the sensor box and the interactions of the airflow on the sensors surfaces at different temperatures are studied by computational fluid dynamics (CFD) tools. Higher simulation accuracy is achieved by optimizing meshes, meshing the computational domain using a fine unstructural tetrahedron mesh. An optimum temperature, 30 °C, is obtained by analyzing the distributions of velocity streamlines and the static pressure, as well as the flow-induced forces over time, all of which may be used to improve the identification accuracy of the electronic-nose for achieving stable and repeatable signals by removing the influence of temperature. [ABSTRACT FROM AUTHOR]

This paper presents the analysis, design, and experimental validation of parameter identification of battery/supercapacitor (SC) hybrid energy storage system (HESS) for the purpose of condition monitoring and maximum power estimation. The analytic bounds on the error of battery and SC parameter identification, considering voltage measurement noise, are obtained based on the Fisher information matrix and Cramer–Rao bound analysis. The identification of different parameters requires different signal patterns to ensure high accuracy, rendering tradeoffs in the multiparameter identification process. With an appropriately designed current profile, HESS parameters are identified using recursive least squares with a forgetting factor. The identified parameters are then used to estimate the maximum power capability of the HESS. The maximum power capabilities of the battery and SC are estimated for both 1 and 30 s time horizons. The parameter identification algorithm can be applied to systems including either batteries or SCs when the optimal excitation current can be injected. Experimental validation is conducted on an HESS test-bed, which shows that the proposed algorithm is effective in estimating the HESS maximum power based on appropriate current excitation. [ABSTRACT FROM AUTHOR]