Variable Threshold Value Research Articles

Variable Capacitor Voltage Threshold Value based Power Loss-Balancing Control for MMCs under Grid Voltage Imbalance

Variable Capacitor Voltage Threshold Value based Power Loss-Balancing Control for MMCs under Grid Voltage Imbalance

A novel approach to structural anisotropy classification for jointed rock masses using theoretical rock quality designation formulation adjusted to joint spacing

Rock quality designation (RQD) has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling. Anisotropy index of jointing degree (AIjd) was formulated by Zheng et al. (2018) by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space. In accordance with spacing terminology by ISRM (1981), defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD. To overcome this limitation, theoretical RQD (TRQDt) introduced by Priest and Hudson (1976) can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution. Anisotropy index of the jointing degree was improved using TRQDt which was adjusted to wider joint spacing by considering Priest (1993)'s recommendation on the use of variable threshold value (t) in TRQDt formulation. After applications of the improved anisotropy index of a jointing degree (AIjd′) to hypothetical jointed rock mass cases, the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AIjd′ formulation by considering the ratings of persistency of joints as proposed by Bieniawski (1989)'s rock mass rating (RMR) classification. Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree (W_AIjd′). A structural anisotropy classification was developed using the RQD classification proposed by Deere (1963). The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.

Quantifying temporal variability and spatial heterogeneity in rainfall recharge thresholds in a montane karst environment

Quantifying rainfall recharge thresholds, including their spatial and temporal heterogeneity, is of fundamental importance to better understand recharge processes and improving estimation of recharge rates. Caves provide a unique observatory into the percolation of water from the surface to the water table at the timescale of individual rainfall recharge events. Here, we monitor nine infiltration sites over six years at a montane cave site in south eastern Australia. Six of the drip hydrology time series have up to ~100 hydrograph responses to rainfall over the monitoring period, three sites do not respond to rainfall events. We use two approaches to quantify rainfall recharge thresholds. At an annual timescale, for all nine drip sites, the total annual percolation water volume was determined for each year of data. Daily rainfall recharge thresholds were then determined by maximising the correlation of annual percolation water volume and total precipitation above a variable daily threshold value. The annual recharge amount methodology produced rainfall recharge thresholds for seven sites, where high and significant correlations (rank correlations > 0.75) occur for daily precipitation thresholds between 6 mm and 38 mm/day. No rainfall recharge thresholds could be obtained from one site which had a low and constant annual drip amount, and from one site which exhibited ‘underflow’ behaviour. At an event timescale, for the six sites which had a hydrograph response to rainfall, the 7-day antecedent rainfall amounts were determined. Minimum 7-day precipitation amounts prior to a hydrograph response for specific drip sites were in the range 13–28 mm and 75% of all recharge events had a 7-day antecedent precipitation between 20.7 and 38.1 mm. Combining all drip water monitoring sites and analysing the data by month identifies a seasonal variability in the minimum 7-day antecedent precipitation necessary to generate potential recharge, from 15 to 25 mm in winter to >50 mm in February and March. We apply a simple water budget model, driven by P and ET and optimised to the observed potential recharge events, to infer a ‘whole cave’ soil and epikarst storage capacity. This storage capacity is between ~50 mm (using potential evapotranspiration, 92% of events simulated successfully) to ~60 mm (using actual evapotranspiration, 79% of events simulated successfully). Modelling of individual drip sites identifies spatial heterogeneity in soil and epikarst storage capacities. Our approach using multiple methodologies allows the comparison between both daily and weekly rainfall recharge thresholds and modelled soil and epikarst storage for the first time.

Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease.

Improving Power Output of Battery and Mode Switching Frequency Based on Real-Time Average Power Method for Multi-Mode Hybrid Energy Storage System in Electric Vehicles

In this paper, a power-split strategy based on a real-time average power method is developed for improving power output of battery and mode switching frequency of a multi-mode hybrid energy storage system (HESS) in electric vehicles. To achieve mode switching and power distribution for the multi-mode HESS, a rule-based strategy is designed based on the high-frequency power demand. Furthermore, a simple real-time average power method is adopted to process the high-frequency power demand. Then, the real-time average power is used as a variable logic threshold value for the power-split strategy. Since the power-split controller responds to the smooth average power rather than the high-frequency power demand, the high-frequency mode switching of the multi-mode HESS can be avoided. The ultra-capacitor works as an enhanced low-pass power filter and the battery can supply smooth and steady output power to the motor inverter. Comparative simulations between the developed power-split strategy and the rule-based strategy are performed. The advantages of the developed power-split strategy for improving the power output of the battery and the mode switching frequency of the multi-mode HESS in electric vehicles are indicated under three typical driving cycles. Moreover, the longer time duration of the real-time average power is designed, the smoother power output of the battery and the lower mode switching frequency of the multi-mode HESS can be achieved.

A New Approach for Generating Human Biometeorological Information Based on Gridded High-Resolution Data (Basic Data of Test-Reference-Years)

The assessment of human-biometeorological information requires appropriate preparation of data and suitable visualisation of results. Human-biometeorological information can be valuable for tourists and visitors, but also for citizens looking for information about their neighbourhood or a new residence. Cities or health resorts can also promote their climate conditions for health rehabilitation. To derive this human-biometeorological information in a unified, comprehensive, and comprehensible form, a tool was developed. The input information contains the coordinates of a place and/or area of interest, and the time period of data chosen by the user. For meteorological data, the basic dataset of Test-Reference-Years from the German Meteorological Service is used, containing hourly meteorological data for the time period from 1995 to 2012, covering Germany with a spatial resolution of 1 km². Based on the Perceived Temperature as a thermal index, days with heat stress and cold stimulus are identified. In this process, the effects of short-term human acclimatisation on the thermal environment are considered by using a variable threshold value based on the thermal conditions of the last 30 days. The results of the tool’s application consist of several frequency diagrams, the Climate-Tourism/Transfer-Information-Scheme, a diagram of heat waves, and maps of the area of interest, displaying the spatial distribution of heat stress and cold stimulus. As an example, the (bio-)meteorological conditions of the region of southern Baden around Freiburg and the Black Forest, including the health resort, Hinterzarten, are analysed.

Locomotive wheel wear simulation in complex environment of wheel-rail interface

Affected by the traction/braking, creep control and wheel-rail interface environment, wheel wear of locomotives is more complicated than that of other rolling stock. A locomotive wheel wear simulation model is developed, which includes a 3D locomotive track coupling dynamics model, a rolling contact model for rail traction vehicles based on Kalker's simplified theory, a PI-based creep control model and a wheel material wear model based on frictional power. The advantages of the dynamics model built in this paper are evaluated through two typical simulation cases. The effects of traction/braking and creep control on wheel wear are comprehensively investigated. The results indicate that the natural vibration characteristics of the locomotive/track system can be well reflected, and their influence on wheel wear can be taken into account. Traction and braking have a great influence on wheel wear, which should be considered for locomotive wheel wear simulation. It is a better way to control creep by using a control mode with variable creep threshold value.

Rapid classification of archaeological ceramics via laser-induced breakdown spectroscopy coupled with random forest

A laser-induced breakdown spectroscopy (LIBS) technique coupled with random forest (RF) was developed and used to classify ceramics from different dynasties. LIBS spectra of the ceramic surfaces were collected, and the major elements (Fe, Ca, Si, Al and Mg) in the ceramics identified using the NIST database. To obtain a better classification results, the LIBS spectra were subjected to five different pre-processing techniques (normalized by maximum integrated intensity, by extremum integrated intensity, mean centering, first-order derivative and second-order derivative). The input variables for RF modeling were selected and optimized by different variable importance threshold values (from 0 to 0.20) and four assessment criteria of out-of-bag (OOB) error, sensitivity, specificity and accuracy. LIBS spectra pre-processed by mean centering with a variable importance threshold value of 0.02 as the input variable were used to construct a RF classification model for different dynasty ceramics. Finally, the classification performance of the RF model was verified by the train set, OOB data and test set. The sensitivity, specificity and accuracy obtained by the RF model for the ceramics samples of test set were 0.8528, 0.9710 and 0.9433, respectively, which indicated a good classification performance.

A prediction method for the cellphone replacement: Rough set-based analysis

Owing to the communication customers’ data are large scale, inconsistent and incomplete, it is hard to predict who will replace their cellphones in the near future. In order to solve this problem, a prediction method based on dominance-based rough set approach (DRSA) is proposed in this paper. Firstly, the customers’ features are extracted based on experts’ knowledge. So, we get the customer decision table. After that, the maximum certainty of each class union is used as the corresponding variable threshold value to control the decision rule extraction program. Therefore, the extracted decision rules can reflect the data characteristics effectively. In this process, it can learn knowledge automatically without setting the threshold values of consistency level depending on prior domain knowledge or by a complex tail-and-error procedure. Additionally, it strengthens the performance for dealing with inconsistent information systems. The efficiency of this method is illustrated by simulation experiments.

Binary expansion based denoising algorithm for an image corrupted by Gaussian noise

Efficient filtering technique on removal of Gaussian noise is not an easy task. In this paper two same centered different size windows are taken for every pixel of the noisy image. A variable threshold value (Γ) has been calculated with the help of standard deviation (σ) of the pixels in the big window. The pixels of the big window are distributed in four sets to determine the conditional mean. Finally an eight bit binary table is created for decision and restoration. Results of the computer simulations demonstrate the effectiveness of the proposed method taking different test images at variable noise densities.

Dynamic Threshold Based Load Balancing Algorithms

In this scenario, dynamic and decentralized Load Balancing (LB) considers all the factors pertaining to the characteristics of the Grid computing environment. Dynamic load-balancing algorithms attempt to use the run-time state information to make more informative decisions in sharing the system load and in decentralization, algorithm is executed by all nodes in the system and the responsibility of LB is shared among all the nodes in the same pool. For this purpose, in this work, an extensive survey of the existing LB has been done. A detailed classification and gap analysis of the existing techniques is presented based on different parameters. The issue of LB in a Grid has been addressed while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, a hierarchical LB technique has been analyzed based on variable threshold value. The load is divided into different categories, like, lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity etc. Poisson process has been used for random job arrival and then load calculation has been done for assigning job to the appropriate Processing Entity for balancing the load in the pool. After balancing the load, it comes into the normally loaded pool, and then Job Migration process is executed. The performance of the proposed model, algorithms and techniques has been examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed techniques over the existing techniques.

Research on Image Denoising with an Improved Wavelet Threshold Algorithm

Wavelet denoising is a commonly used method in the field of image denoising algorithms, based on the analysis of the characteristics of estimated wavelet coefficients in the wavelet threshold denoising and the traditional soft threshold and hard threshold method, in the light of the discontinuity of hard threshold and fixed deviation of soft threshold, and non-differentiable of compromise algorithm, a wavelet threshold denoising algorithm is put forward which is continuous based on the wavelet coefficients and differentiable and variable threshold value deviation, the experimental results show that this method has better denoising effect and has good practical value.

Variable threshold-based hierarchical load balancing technique in Grid

Load balancing is an important aspect of Grid resource scheduling. This paper attempts to address the issue of load balancing in a Grid, while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, to its optimum value, a hierarchical load balancing technique has been analysed based on variable threshold value. The load is divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity. The proposed model has been rigorously examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed technique over existing techniques, such as without load balancing, load balancing in enhanced GridSim.

Using Stressor–Response Models to Derive Numeric Nutrient Criteria for Lakes in the Eastern Plain Ecoregion, China

AbstractLakes in the Eastern Plain ecoregion of China are typically shallow, experience large disturbances, and are hydrologically connected to rivers. In recent years, lake eutrophication in this ecoregion has intensified due to human activities. To determine important regional nutrient criteria, the effects of hydrologic features and disturbances should be taken into account. In this paper, annual average data were used to construct stressor–response models for each hydrological lake type identified for lakes in the Eastern Plain ecoregion. In these models, total nitrogen (TN) and total phosphorus (TP) criteria were obtained from a chlorophyll a (Chl‐a, response variable) threshold value. A Chl‐a concentration of 4.73 μg L−1 was identified as this threshold value, which was based on the cyanotoxin limits associated with drinking water supply. The three lake types, which differed in hydrological structure, had significantly different TN and TP criteria. For example, lakes connected to the Yangtze River (T1) had a TN criterion ranging from 0.594 to 0.838 mg L−1, which was substantially higher than those for lakes that were never connected to the river (T3, 0.234–0.422 mg L−1). The exception to this trend occurred for lakes that were historically but not currently connected to the river (T2) due to P‐limitation in this lake type. However, there was little variability in the TP criterion among T1, T2, and T3 lakes, the values of which ranged from 0.021 to 0.037, 0.023 to 0.042, and 0.028 to 0.041 mg L−1, respectively.

Data Reconciliation and Suspect Measurement Identification for Gas Turbine Cogeneration Systems

Data reconciliation is widely used in the chemical process industry to suppress the influence of random errors in process data and help detect gross errors. Data reconciliation is currently seeing increased use in the power industry. Here, we use data from a recently constructed cogeneration system to show the data reconciliation process and the difficulties associated with gross error detection and suspect measurement identification. Problems in gross error detection and suspect measurement identification are often traced to weak variable redundancy, which can be characterized by variable adjustability and threshold value. Proper suspect measurement identification is accomplished using a variable measurement test coupled with the variable adjustability. Cogeneration and power systems provide a unique opportunity to include performance equations in the problem formulation. Gross error detection and suspect measurement identification can be significantly enhanced by increasing variable redundancy through the use of performance equations. Cogeneration system models are nonlinear, but a detailed analysis of gross error detection and suspect measurement identification is based on model linearization. A Monte Carlo study was used to verify results from the linearized models.

Improved real-time method for noise reduction of directional Doppler audio signals

A noise reduction filter for Doppler audio signals is one important part of Transcranial Doppler ultrasound system. Several studies have been carried out for low-pass filter with fixed value for Doppler audio signals; however, most of these studies lacked complex research works and few studies analyzed the relationship of both parts of spectrum signal processing and audio signal processing. To solve the above-mentioned concern, this research is focused on the algorithms of de-noising and enveloping of spectrum signal processing part and improved new real-time method, which reduces the noise using low-pass filter with a variable threshold value based on maximum frequency of spectrum signals. The noise of Doppler audio signals was reduced efficiently by using this algorithm.

Polygonal approximation of digital planar curves through break point suppression

This paper presents a new algorithm that detects a set of dominant points on the boundary of an eight-connected shape to obtain a polygonal approximation of the shape itself. The set of dominant points is obtained from the original break points of the initial boundary, where the integral square is zero. For this goal, most of the original break points are deleted by suppressing those whose perpendicular distance to an approximating straight line is lower than a variable threshold value. The proposed algorithm iteratively deletes redundant break points until the required approximation, which relies on a decrease in the length of the contour and the highest error, is achieved. A comparative experiment with another commonly used algorithm showed that the proposed method produced efficient and effective polygonal approximations for digital planar curves with features of several sizes.

DEVELOPMENT OF AN ORTHOGONAL DOUBLE-IMAGE PROCESSING ALGORITHM TO MEASURE BUBBLE VOLUME IN A TWO-PHASE FLOW

In this paper, an algorithm to reconstruct two orthogonal images into a three-dimensional image is developed in order to measure the bubble size and volume in a two-phase boiling flow. The central-active contour model originally proposed by P. <TEX>$Szczypi\'{n}ski$</TEX> and P. Strumillo is modified to reduce the dependence on the initial reference point and to increase the contour stability. The modified model is then applied to the algorithm to extract the object boundary. This improved central contour model could be applied to obscure objects using a variable threshold value. The extracted boundaries from each image are merged into a three-dimensional image through the developed algorithm. It is shown that the object reconstructed using the developed algorithm is very similar or identical to the real object. Various values such as volume and surface area are calculated for the reconstructed images and the developed algorithm is qualitatively verified using real images from rubber clay experiments and quantitatively verified by simulation using imaginary images. Finally, the developed algorithm is applied to measure the size and volume of vapor bubbles condensing in a subcooled boiling flow.

354 可変しきい値による耳小骨連鎖異常の三次元画像

354 可変しきい値による耳小骨連鎖異常の三次元画像

Development of Digital Image-Processing Algorithm to Compute Unified Crack Index for Salt Lake City

This paper presents the recent efforts in developing an image processing algorithm for computing a unified pavement crack index for Salt Lake City. The pavement surface images were collected using a digital camera mounted on a van. Each image covers a pavement area of 2.13 m (7 ft) × 1.52 m (5 ft), taken at every 30-m (100-ft) station. The digital images were then transferred onto a 1-gigabyte hard disk from a set of memory cards each of which can store 21 digital images. Approximately 1,500 images are then transferred from the hard disk to a compact disc. The image-processing algorithm, based on a variable thresholding technique, was developed on a personal computer to automatically process pavement images. The image is divided into 140 smaller tiles, each tile consisting of 40 × 40 pixels. To measure the amount of cracking, a variable threshold value is computed based on the average gray value of each tile. The program then automatically counts the number of cracked tiles and computes a unified crack index for each pavement image. The crack indexes computed from the image-processing algorithms are compared against the manual rating procedure in this paper. The image-processing algorithms were applied to process more than 450 surveyed miles of Salt Lake City street network.