Abnormal Points Research Articles

Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental Training

In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting.

Hemisphere opposite to central retinal vascular trunk deviation is earlier affected by glaucomatous damage in primary angle-closure glaucoma.

The purpose of the study was to investigate whether the position of the central retinal vascular trunk (CRVT), as a surrogate for lamina cribrosa (LC) offset, is associated with the dominant hemisphere of visual field defect in primary angle-closure glaucoma (PACG) eyes. Central retinal vascular trunkdeviation was measured from Bruch's membrane opening (BMO) centre, which was delineated by OCT imaging, using the horizontal midline as a reference. The dominant hemisphere developing visual field defect was defined as three connected abnormal points (having a p-value < 5% probability of being normal) appearing in only one hemisphere or each point of the hemisphere having a statistically worse value compared with its mirrored point in the opposite hemisphere on pattern deviation plots. One hundred five (80%) of 132 eyes with PACG had dominant hemisphere of visual field defect initially: 70 eyes (67%) in the superior and 35 eyes (33%) in the inferior hemisphere. The CRVT was located superiorly in the dominant superior visual field defect group (p < 0.001). A logistic regression analysis revealed that superior deviation of the CRVT was the only factor associated with dominant superior visual field defect (p < 0.001). Externally oblique border (EOB) presence was associated with larger BMO (p=0.005) and angular deviation of CRVT (p=0.002). Central retinal vascular trunk deviation was associated with the dominant hemisphere of visual field defect in PACG eyes. This finding implies that the LC position relative to the BMO centre (LC/BMO offset) may incur structural vulnerability in the optic nerve head of PACG eyes.

Acute Cerebellitis in A Case of Systemic Sclerosis overlap with Systemic Lupus Erythematosus: Image study

Systemic lupus erythematosus, and systemic sclerosis are chronic autoimmune diseases with symptoms with different degrees of severity and mechanisms. Hence, we present a 36-year-old female with a history of systemic lupus erythematosus which was later overlapped with systemic sclerosis. She complained of vertigo over three months before developing poor gait. She was diagnosed with truncal ataxia due to her ataxic gait and abnormal point motions. Based on the magnetic resonance imaging, a final diagnosis of a cute cerebellar dysfunction in systemic lupus erythematosus was made. Cyclophosphamide was initiated with a single dose of 500 mg which continued for at least three more monthly cycles, and the methylprednisolone pulse was adjusted to 500 mg/day for three days. Due to measures taken to avert a renal crisis caused by scleroderma, the methyl prednisolone dose was deemed to be lower than the adjusted amount (1 g). A follow-up magnetic resonance imaging showed regression of the lesions after treatment, the cerebral lesion had shrunken, and corticomedullary lesion had resolved entirely. Cerebral ataxia could be developed in patients with sytemic lupus erythematosus in terms of vasculitis, diffusely infiltrating glioma, acute disseminated encephalomyelitis, and systemic lupus erythematosus itself. One of the uncommon signs of neuropsychiatric systemic lupus erythematosus is ataxia and cerebellar involvement. The prognosis is generally favorable while receiving immunosuppressive therapy.

Coupling of multi-hydrochemical and statistical methods for identifying apparent background levels of major components and anthropogenic anomalous activities in shallow groundwater of the Liujiang Basin, China.

Natural background levels (NBLs) is a prerequisite for distinguishing anthropogenic groundwater pollution and judging the evolution of groundwater quality. However, due to regional differences of hydrogeochemitry and water-rock interaction, coupled with long-term anthropogenic activities, it is no longer accurate to assess NBLs with only statistical methods or without considering human impact. Herein, multi-hydrochemical and statistical methods were examined to identify apparent background levels and anthropogenic anomalous activities of shallow groundwater by selecting Liujiang Basin as a study area. The results showed that the differences in hydrochemical characteristics among each hydrogeological unit (HU) fully illustrated the necessity of rationally dividing HU for background value identification. The application of the concept of apparent background levels (ABLs), that is, incorporating normal human activities into the background levels, efficiently solved the problem of being unable to obtain pristine NBLs due to long-term human activities. The coupling of Hydrochemistry and Grubbs' test (Hydro-Grubbs) was confirmed as the optimal method in identifying and eliminating anthropogenic groundwater anomalies, performing sufficiently superiority when compared with purely statistical methods. It is mainly because the Hydro-Grubbs method not only considers the discreteness of the data itself, but also considers the internal connection and evolution process of the hydrochemical compositions. For the eliminated abnormal points, 91.0-93.6% of which have been effectively explained by pollution percentage index and the impact of coal mining, industrial activities, residents, agricultural activities, and septic tanks leakage, proving the rationality and reliability of Hydro-Grubbs method and ABLs evaluation result. This finding will assist in accurately identifying anthropogenic pollution on a regional scale and guiding future efforts to protect groundwater resources.

A Position Fixing Method for Near-Bottom Camera Data on the Seafloor

The submarine camera system is one of the most effective detection methods for detecting seabed hydrothermal sulfide. The position fixing of camera equipment is generally achieved by the ultra-short baseline (USBL) positioning systems. The dynamic changes in the marine environment and the loss of acoustic signals lead to many abnormal points in the USBL positioning data. The existing methods cannot perform position fixing correction for USBL positioning data with many scattered abnormal points. This paper selects and focuses on the seabed camera data of the XunMei mineralization area acquired from the China South Atlantic voyage and employs the DTW optimization algorithm to solve this problem. This method is implemented based on Python language and an ArcGIS technical environment, and a total of 97,469 topographic profiles with 10 m spacing are extracted from the topographic data of the object area with 1m accuracy. Moreover, the highest similarity (95.9%) is obtained between the bathymetric profile of the camera survey line and the above topographic profile. The results indicate that the proposed algorithm is fast and effective, and the positioning correction accuracy of the deep-sea near-bottom camera data can reach the meter level (determined by the topographic data accuracy). Moreover, the solution can provide services for accurately position fixing near-bottom exploration equipment and its exploration data. It can also solve the problem of underwater position fixing problem that a GPS cannot solve.

WPMAVM: Weighted plus-and-minus allowance variance minimization algorithm for solving matching distortion

• A weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed. • WPMAVM takes the plus-and-minus allowance in the objective function of VMM into account. • The distance function is introduced to suppress the distortion caused by the abnormal allowance. • WPMAVM has better robustness to minus allowance and abnormal point clouds compared to ICP and VMM. • The proposed algorithm is suitable for calibration and measurement of complex curved surfaces. Point cloud matching is widely used in the calibration and measurement of complex curved surfaces, and the matching accuracy affects both the subsequently machined surface quality and measurement results. In the actual application, the existing iterative closest point (ICP) algorithm is prone to matching distortion when the measurement point clouds have inherent defects such as unclosed shape, uneven density and Gaussian noise . Despite this problem can be solved by the variance-minimization matching (VMM) algorithm, but the point clouds match distortedly both for ICP and VMM particularly when the workpieces behave with complex and unknown allowance distribution. For examples, the car bodywork is characterized by remarkable flexibility with local deformation, resulting in abnormal allowance, while the machined engine blade shows over- and less-cutting induced minus and abnormal allowances in high and low curvature areas, respectively. In this paper, a weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed to overcome these problems. Specifically, the plus-and-minus allowance in the objective function of VMM algorithm is subdivided into the sum of plus-and-minus allowance variance minimization to suppress the distortion caused by the plus-and-minus allowance. The weighted term w , defined as the distance function from the point to the weighted plus-and-minus mean, is introduced to suppress the distortion caused by the abnormal allowance. The simulation and experimental comparisons on three typical workpieces show that the proposed algorithm has better robustness to minus allowance and abnormal point clouds compared to the existing ICP and VMM algorithms.

Statistical Inference of Spatial Econometric Models Based on Likelihood Functions

This paper is devoted to studying the probabilistic and statistical properties of maximum likelihood estimation of parameters in spatially mixed autoregressive models. When the response variable in the mixed autoregressive model obeys a continuous distribution, this paper verifies the monotonicity of the likelihood function of the mixed autoregressive model with respect to the autoregressive parameter P, which proves the existence and uniqueness of the maximum likelihood estimation of the autoregressive parameter. The results show that when the condition n &gt; rank(x+1) is satisfied, the quasi-likelihood function of the mixed autoregressive model has a unique maximum value with probability 1 in the parameter space; when n &gt; rank(x+1) and the regression coefficient When the matrix column is full rank, the maximum likelihood estimates of all parameters in the spatial mixed autoregressive model exist with probability 1 and are unique in the parameter space. In order to detect the strong influence points and abnormal points in the spatial mixed autoregressive model, this paper uses the first derivative method in the local influence analysis to obtain the statistics of the strong influence points and abnormal points in the spatial mixed autoregressive model in the form of variance perturbation. Simulation studies have shown that the first derivative of the maximum likelihood estimate of variance in the spatial mixed autoregressive model should be chosen to obtain the test statistic, which can effectively avoid the mearing and masking effects that often occur and are difficult to handle in local influence analysis. . As application and verification, this paper analyzes a real data to show that the conclusions obtained in this paper are reliable and practical.

Risk factors for glaucoma are reflected in abnormal responses to frequency-doubling technology screening in both normal and glaucoma eyes

The frequency-doubling technology (FDT) screening test (FDT-C-20-1) has adopted in many recent population-based glaucoma surveys, but factors associated with false-positive (FP) responses to FDT-C-20-1 in normal eyes and false-negative (FN) responses in glaucoma eyes were not known. These factors were investigated in a population-based setting using the data from 3805 normal eyes (2381 subjects) and 272 eyes with definite glaucoma (215 subjects) in the Kumejima Study participants with reliable FDT-C-20-1 results. Considering the presence of at least one abnormal test point (P < 0.01) as abnormal, the specificity and sensitivity of FDT-C-20-1 for glaucoma were 91.8% (95% confidence interval, 91.1 ~ 92.5) and 56.3% (47.0 ~ 62.5), respectively. Multivariate linear mixed-model logistic regression analysis showed correlations with older age, worse visual acuity, greater β-peripapillary area (P < 0.001 for all comparisons) and more myopic refraction (P = 0.030) with the FP responses in normal eyes, and normal-tension glaucoma (P = 0.043), a better mean deviation value of Humphrey perimetry (P = 0.001), larger rim area (P = 0.041), and absence of disc hemorrhage (P = 0.015) with the FN responses in glaucoma eyes. In a population-based setting, abnormal responses to FDT-C-20-1 indicate the presence of a risk factor for glaucoma in normal eyes and risk factors for more rapid progression in glaucoma eyes.

Siltuximab is associated with improved progression-free survival in idiopathic multicentric Castleman disease

AbstractIdiopathic multicentric Castleman disease (iMCD) is a rare heterogeneous disorder involving multicentric lymphadenopathy, systemic inflammation, and cytokine-driven organ dysfunction. Despite the approval of siltuximab, a monoclonal antibody against interleukin-6, for the treatment of iMCD, it is not known how long patients should receive siltuximab before determining whether the treatment is beneficial and should be continued. We performed post hoc analyses of the phase 2 randomized double-blind placebo-controlled trial of siltuximab for the treatment of patients with iMCD to determine the sequence of normalization of laboratory, clinical, and lymph node responses in patients who responded to siltuximab. Seventy-nine patients were enrolled in the trial (siltuximab, n = 53; placebo plus best supportive care, n = 26). Progression-free survival (PFS) was significantly improved in siltuximab-treated patients compared with those receiving placebo (P = .0001). The median PFS was 14.5 months (95% confidence interval, 13.6 months to upper bound not reached) for patients receiving placebo but was not reached for patients receiving siltuximab. In siltuximab-treated patients who achieved durable tumor (radiologic) and symptomatic responses (18 [34%] of 53), the median time to normalization of abnormal laboratory tests and clinical end points occurred in the following sequence: thrombocytosis, symptomatic response, elevated C-reactive protein, hypoalbuminemia, anemia, lymph node response, hyperfibrinogenemia, and elevated immunoglobulin G. Siltuximab treatment prolongs PFS, rapidly improves symptomatology, and provides meaningful clinical benefit despite some laboratory tests and enlarged lymph nodes taking months to normalize in treatment responders. These data support the continued frontline use of siltuximab for iMCD, as recommended by international guidelines. This trial was registered at www.clinicaltrials.gov as #NCT01024036.

A Modified Mahalanobis Distance Metric Used to Optimize Mahalanobis Space and Improve Classification Performance of MTS

The Mahalanobis–Taguchi system (MTS) is a diagnostic and forecasting technique in a multidimensional system that integrates the Mahalanobis distance (MD) and robust engineering of Taguchi. To implement MTS, a set of observations from a normal group is selected to construct the Mahalanobis space (MS). With this MS as a reference, new observations from an unknown group can be judged to be normal or abnormal, and the degree of abnormality can be determined. MD is very sensitive to data changes, so the data quality of normal samples used to construct the MS directly affects the accuracy of classification. In practical applications, the selection of normal samples depends on the experience and subjective judgment of experts and lacks an objective selection mechanism. In this paper, a modified MD metric is proposed, which is combined with the individual control chart to obtain a robust MS. First, the initial MS is constructed according to the normal samples selected by experts, and the MD of each normal sample is calculated by the initial MS. Then, the MD of each normal sample in the corresponding reduced MS is computed, and the incremental MD is used as the new distance metric to establish the individual control charts. The stability rules of the control chart are employed to eliminate abnormal points, and the MS of the stable state is obtained. To evaluate the effectiveness of the modified MD metric, a numerical simulation experiment is implemented, and the results show that the proposed method is effective and improves the classification performance of MTS. Finally, the improved MTS method is applied to a real medical diagnosis case.

A novel mobile navigation monitoring system based on single photon ionization time-of-flight mass spectrometry: Instrumentation development, performance and application

Tracing the sources of volatile organic compounds (VOCs) pollution plays an important role in controlling the emission of VOCs and reducing the content of O3 and secondary aerosols. In this paper, a novel mobile navigation monitoring system was developed, which can be used to monitor VOCs pollution of whole area and to locate VOCs pollution sources at abnormal points, providing an available technical methods for rapid and efficient identify of pollution sources in the area. The navigation monitoring system is composed of modified vehicle with a single photon ionization time-of-flight mass spectrometer (SPI-MS-2000), global positioning system receiver and other instruments. The navigation monitoring system monitors along the route in the target area, and establishes the pollution map of the whole area. According to the pollution map, the emission sources of VOCs can be accurately traced. The spatial resolution of the navigation monitoring system is 27 m. The resolutions of the benzene, toluene, xylene and chlorobenzene were 746, 740, 717 and 716, respectively. The limits of detection of the SPI-MS-2000 down to 2.46, 0.95, 0.38 and 1.13 ppbv were obtained for benzene, toluene, xylene and chlorobenzene. The SPI-MS-2000 was calibrated by using PAMS and TO-15. To evaluate the ability of the navigation monitoring system to monitor VOCs in the atmospheric environment, the distribution of VOCs on two different routes were investigated, three abnormal positions were found in one of the routes, which provided data support for the environmental pollution control in this area.

Research on Image Texture Feature Extraction Based on Digital Twin

The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points.

Product specification and economic benefit maximization prediction model of a coal preparation plant in Datong, Shanxi

ABSTRACT Due to the adjustment of the Chinese industrial structure in recent years, the coal industries are facing a sluggish market environment. Therefore, it is important to study the product specification prediction and economic benefit maximization prediction of coal preparation plants. First, the washability data and distribution curve models were used to calculate the quantity and quality index of the products of the shallow slot heavy medium separator, the heavy medium cyclone, and the spiral separator, respectively. A gravity separation prediction model was built and the average fitting error of the model was 2.89%. Second, the calorific value – (ash and sulfur) model was built, in which SPSS was used to eliminate abnormal points. A mathematical model of the price of clean coal – calorific value was built. Finally, a genetic algorithm was used to optimize each sorting process to find the best sorting density of each sorting system: the result of the shallow slot heavy medium separator was 1.72 g/cm3, that of the heavy medium cyclone was 1.99 g/cm3, and the result of the spiral separator was 1.86 g/cm3. The maximum sales revenue of raw coal per ton from the economic benefit maximization prediction model was 379.79 yuan. The models provided forward-looking guidance for the related adjustments of production.

Comparison between frequency-doubling technology perimetry and standard automated perimetry in early glaucoma

This study aimed to find out the significance of the difference between frequency-doubling technology perimetry (FDT) and standard automated perimetry (SAP) in terms of the detected visual field (VF) damage, and evaluate associated factors to SAP–FDT difference in early glaucoma. Glaucoma patients in early stage (MD better than − 6.0 decibel, 96 eyes) were included in this cross-sectional study. We subtracted mean deviation (MD) and pattern standard deviation (PSD) of FDT from those of SAP, respectively. Additionally, we counted significantly depressed points of P < 5% and P < 1% on the pattern deviation probability plot of both FDT and SAP and defined eyes with significant SAP–FDT difference when the number of abnormal points were greater than 4 points on FDT. We measured lamina cribrosa depth (LCD) and lamina cribrosa curvature index (LCCI) for structural parameters of the optic nerve head from images using enhanced depth imaging of the optical coherence tomography (OCT). Peripapillary vessel density (VD) and presence of microvasculature dropout (MvD), the complete loss of choriocapillaris in localized regions of parapapillary atrophy, was evaluated using deep layer map of OCT angiography (OCT-A) for vascular parameters. Peripheral nasal step (PNS) group had an isolated glaucomatous VF defect within nasal periphery outside 10° of fixation. Parafoveal scotoma (PFS) group had an isolated glaucomatous VF defect within 12 points of a central 10˚ radius. Eyes with significant SAP–FDT difference showed higher detection of MvD on deep layer map of OCT-A, greater LCD, and greater LCCI (all P < 0.05, respectively). In logistic regression analysis, frequent presence of MvD, less presence of disc hemorrhage, and greater LCD were significantly associated with significant SAP–FDT difference. Sub-analysis was performed in eyes with PNS (50 eyes) and PFS (46 eyes). SAP–FDT difference of MD value showed positive association with peripapillary VD on deep layer of OCT-A, which was significant in eyes with PFS compared to eyes with PNS. SAP–FDT difference of PSD value showed negative association with LCCI and LCD, which was significant in eyes with PNS compared to eyes with PFS. Glaucomatous eyes classified by the difference of the detected VF damage on FDT versus SAP showed different clinical features. Greater SAP–FDT difference was significantly associated with structural parameters such as LCD and LCCI. Less SAP–FDT difference was associated with presence of disc hemorrhage and lower deep layer peripepillary VD. There is possibility to use the difference of SAP and FDT to identify associated risk factors in glaucoma patients.

An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

ABSTRACT The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm.

DuLoc: Dual-Channel Convolutional Neural Network Based on Channel State Information for Indoor Localization

With the increasing demand for location-based services in indoor environments, research on indoor localization technology has become an urgent need. In complex indoor environments, strong multipath effects can greatly interfere with the propagation of wireless signals, which may seriously degrade localization accuracy. To solve the problem, this paper proposes an indoor localization system DuLoc based on fine-grained subcarriers and a dual-channel convolutional neural network (CNN). In this system, abnormal sampling points are removed by the Isolation Forest method to deal with environmental noise. In order to select more stable and effective signals, the number of clusters of each subcarrier is analyzed by the Mean Shift clustering method, and the fingerprint database is divided into two sub-databases according to the threshold. The dual-channel CNN model is constructed to optimize the classification results with the data characteristics. Those two types of subcarrier data construct the input feature images to train the dual-channel CNN model. Then the final location coordinate is calculated by the probability weighted centroid method. Experimental results are presented to demonstrate that DuLoc has higher localization accuracy in complex indoor environments compared with the other existing methods.

Profile analysis with reconstruction robustness for measurement data subject to outliers.

In the surface profile analysis, there are often a few observations that contain outliers. Due to the existence of outliers, the application of non-robust reconstruction algorithms for measurement data will become a huge problem because these methods are often sensitive to outliers and the approximation effectiveness will be greatly aggravated. In view of this, this paper presents a novel angle-based moving total least squares reconstruction method, to the best of our knowledge, that applies two-step pre-treatment to handle outliers. The first step is an abnormal point detection process that characterizes the geometric features of discrete points in the support domain through a new angle-based parameter constructed by total least square. Then, the point with the largest anomaly degree is removed, and a relevant weight function is defined to adjust the weights of the remaining points. After pre-treatment, the final estimates are calculated by weighted total least squares (WTLS) based on the compact weight function. The detection and removal of outliers are automatic, and there is no need to set a threshold value artificially, which effectively avoids the adverse impacts of human operation. Numerical simulations and experiments verify the applicability of the proposed algorithm as well as its accuracy and robustness.

Prediction of Central Visual Field Severity in Glaucoma.

The severity of central visual field (VF) defects on 24-2 VF and related scotomas on 10-2 VF may be predicted by assessing perimetric defects at abnormal central 12 points on 24-2 VF in early glaucoma. Investigating the association between perimetric parameters at abnormal central 12 points on 24-2 VF and the severity of central visual field defects (CVFDs) on 24-2 VF and related parafoveal scotomas on 10-2 VF. We examined 64 eyes of 56 glaucoma patients with CVFDs on 24-2 VF with a mean deviation better than -7 dB and completed 24-2 and 10-2 VF testing within 6 months. On the basis of 10-2 VFs' pattern defects, eyes were grouped into 3: an arcuate parafoveal scotoma, severe defect; partial arcuate, moderate defect; and minimal defect. VF parameters at abnormal points (P<1%) within the central-most 4 and paracentral 8 points on total deviation/pattern deviation plots on 24-2 VF were analyzed to predict the severity of CVFDs. Eyes with arcuate scotoma showed more functional loss than eyes without arcuate scotoma on 10-2 VF (P<0.001). A significant association was observed between abnormal 24-2 VF points' (<1%) threshold sensitivity lower than 20 dB [odds ratio (OR)=7.2; P=0.002 and OR=5.1; P=0.003 for the central 4 and paracentral 8 points, respectively] and defect values worse than -15 dB (OR=8.0 and 5.6 for the central 4 and paracentral 8 points, respectively, P=0.005) with arcuate scotoma on 10-2 VF. Superior nasal defect in the central 5 degrees on 24-2 VF was significantly associated with an arcuate defect on 10-2 VF (P<0.001). Clinicians may predict the severity of CVFDs on 24-2 VF and parafoveal scotomas on 10-2 VF by measuring threshold sensitivities and defect values at abnormal central 12 points (<1%) on 24-2 VF in early glaucoma.

EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge—the slowest step usually is vital in the whole electrochemical process. Twenty global optimization algorithms (GOA)s were assessed on simulated and experimental impedance spectra belonging to nine different ECMs, which proved that GOAs obtained nearly the same identification accuracy as the artificial identification under no interference of obvious abnormal points. ML combining with GOA provides a new possibility to automatically process EIS.

Analysis of Foot-Ground Interaction for Assessment of Gait Using Lab View

Injuries caused by falls are common among athletes. The field of sports mechanics and training are moving towards implementing green technology to evaluate the performance and training of the athletes. There is a need to understand the gait pattern of an individual for better understanding and planning. Gait analyses performed within a laboratory imposes restriction over an individual’s performance, which can influence the gait pattern intended to study. A shoe-integrated sensor system provides the freedom to move the subject freely, while at the same time provides information related to the gait pattern and fall events occur. MEMS sensors integrated shoes are developed to study the gait pattern for wellness monitoring among elders. The fall is detected by comparing the amount of the pressure distribution in the respective sensors with the accelerometer placed around the ankle. In this work, the presence of the abnormality in the gait pattern and the abnormal point are analysed. A shoe system with sensor design and interface with LabVIEW are developed. In case 1, the heel pressure is 35mV, the toe pressure is 9.2mV, acceleration value is 1.3mV and fall is no fall.