Frequency Weighting Research Articles

PERTUMBUHAN DAN HASIL TANAMAN TOMAT (Lycopersicum esculentum MILL.) PADA PEMBERIAN PUPUK ZN DAN JARAK TANAM YANG BERBEDA

Tomato (Lycopersicum esculentum Mill.) is a plant that is rich in benefits, but the yield is still low, some of which are due to a lack of nutrients, one of which is micro nutrient zinc, and improper plant spacing. The research was carried out to determine: 1) the best dose of Zn fertilizer and the right plant spacing; 2) the interaction between Zn fertilizer dosage and plant spacing; 3) the relationship (correlation) between the two observed variables. The research was conducted from July to October 2018 in Teluk Dalam Village, Mulawarman Village, Tenggarong Seberang District, Kutai Kartanegara Regency. The 4x3 factorial experiment was arranged in a Randomized Complete Block Design with three replications. The first factor was the dose of Zn fertilizer, consisting of four levels, namely 0.00; 3.00; 6.00; and 9.00 kg Zn ha-1. The second factor was plant spacing, consisting of: 50 cm x 50 cm; 60 cm x 50 cm; 70 cm x 50 cm. Data were analyzed using analysis of variance, followed by the LSD test at the 5% level. Correlation analysis was used to determine the relationship between two quantitative variables. The results showed that the effect of Zn fertilizer dosage was significantly different on plant height and plant age at first harvest, number of fruit per plant, fruit fresh weight per plant, harvest frequency, and fruit fresh weight per hectare, but not significantly different on plant height and age plants at flowering and the number of branches per plant. The effect of plant spacing was significantly different, on the other hand, the interaction was not significantly different on all observed variables. The best dose of Zn fertilizer is 6.00 kg ha-1 with fruit fresh weight 25.16 Mg ha-1 and the best plant spacing is 70 cm x 50 cm with fruit fresh weight 25.18 Mg ha-1. The results of the correlation analysis showed that fruit fresh weight per hectare had a positive correlation with the number of fruit, fruit fresh weight per plant, and the frequency of harvest with the correlation coefficient (r), respectively 0.7350; 0.8190; and 0.6316, but negatively correlated with plant age at flowering and first harvest, namely -0.2552 and -0.3292.

Four Text‐Mining Methods for Measuring Elaboration

AbstractWhen individuals engage in divergent thinking, they vary on their Elaboration, or the degree to which they explain and embellish their responses. Although Elaboration has been considered relevant to creativity research for decades, its measurement has remained under‐developed. Here, we leverage technical and methodological perspectives from the text‐mining literature to posit four methods for quantifying elaboration: Unweighted Word Count, Stoplisted Inclusion, Part of Speech Inclusion, and Inverse Frequency Weighting. Although the Unweighted Word Count method is becoming typical in the field, more complex weighting methods appear to better fit the conceptualization of Elaboration. We explain the benefits of each of the included methods and demonstrate their application to responses from the Alternate Uses Task: showing that all four of these text‐mining methods produced Elaboration scores with high levels of reliability, but the Stoplisted Inclusion method appeared to maximize the score validity both in terms of criteria correlations and power to discriminate among creative experts and non‐experts. We offer an open‐access module for creativity researchers to apply these methods to their own data via our laboratory website [https://openscoring.du.edu/].

Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms

The present study aims to measure wetland habitat vulnerability (WHV) in moribund deltaic part of India using ten conditioning parameters e.g., WPF, water depth, change in WPF, change in depth, APF, seasonality, fragmentation, distance from river, distance from road, and settlement area. For developing suitable models integrating the data layers, four bivariate models namely, logistic regression (LR) frequency ratio (FR), Shannon entropy (SE), and weights of evidence (WoE), and four machine learning (ML) algorithms namely, artificial neural network (ANN), J48 decision trees (DTs), random forest (RF), and reduced error pruning (REP) tree have been used. Results reveal that the 20 km2 (11.13%) to 35.70 km2 (19.88%) and 18.43 km2 (10.27%) to 29.01 km2 (16.16%) of area to total wetland area has emerged as high and very high habitat vulnerable zones in phase II, whereas, 17.60 km2 (11.72%) to 31.10 km2 (20.75%) and 16.23 km2 (10.82%) to 28.61 km2 (19.07%) of area found as high to very high vulnerable in phase III in case of both bivariate and ML models. Accuracy assessment of using AUC (ROC), Kappa, and overall accuracy have confirmed the acceptability of all the models but result of machine learning based model is more precise than bivariate models. Frequency ratio from bivariate models and REP tree from machine learning models are found to be the most acceptable. Sensitivity analysis shows that WPF factor followed by depth of wetland are the most important contributing factors. So, application of machine learning model for vulnerability study is recommended.

Organized Structure of Ventricular Fibrillation during Prolonged Heart Perfusion in Dogs.

The aim of the study was to identify the organized ventricular fibrillation (VF) activity in the dog heart and characterize its quantitative parameters during prolonged heart perfusion.Materials and Methods.A total of four experiments on isolated dog’s hearts perfused with the blood of a supporting dog were performed. Episodes of VF were recorded in the form of an electrogram followed by a spectral analysis by the fast Fourier transform in the range of 0.5–15 Hz. The frequency, spectral power (amplitude), and relative weight (%) of the 1st, 2nd, and 3rd highest amplitude oscillations were determined (frequency — mode; amplitude, relative weight — M±SEM; n=120).Results.In the perfused dog heart, VF was characterized by an organized activity, as evidenced by the dominant structure of the oscillation frequencies. Oscillations with a frequency of 9–10 Hz (occurring in 1/10 of the 0.5–15 Hz range) represent 42–44% of the spectral power and dominate the structure of the oscillation frequencies. The frequency and spectral power of the dominating oscillations proved to be stable thus indicating that under perfusion, VF did not cause disturbances in the heart organized activity.Conclusion.Our experimentation with isolated perfused hearts revealed the patterns of VF that could not be revealed in situ under conditions complicated by nerve factors and ischemia in VF. The results of the work are protected with a patent which is applicable for VF diagnosis in implantable defibrillators.

Difference thresholds for a vehicle on a 4-poster test rig

Improving vibration-induced discomfort often requires a reduction in the vibration experienced by vehicle occupants. Simulation software and test equipment are able to measure changes in vibration that are too small for humans to perceive. It is therefore important to know how large the change in vibration should be, i.e. the difference threshold, for occupants to perceive an improvement in comfort. This study estimates difference thresholds for ten automotive engineers seated in a vehicle on a 4-poster test rig. Participants were exposed to multi-axis vibration. Component ride values were calculated by applying BS 6841 frequency weightings and multiplication factors to seat accelerations in the six directions. Difference thresholds were estimated for two road profiles using the vertical component ride value and combined point ride value (i.e. the root-sums-of-squares of the six component ride values). The two road profiles had different magnitudes, but the same spectral shape, resulting in median vertical component ride values of 0.58 and 1.01 m.s.−2, root-mean-square. An up-down transformed response rule was used with a three-down-one-up response grouping to estimate difference thresholds at a 79.4% probability level. The median relative difference threshold for the two roads was 10.13% and 8.58% considering the vertical component ride value, and 10.99% and 9.24% considering the combined point ride value. No statistically significant difference was found between the medians of the relative difference threshold over the two roads considering either of the two ride values (p-value = 0.995 in both instances), suggesting that Weber's law holds.

Can multi‐taxa diversity in European beech forest landscapes be increased by combining different management systems?

Abstract Forest management greatly influences biodiversity across spatial scales. At the landscape scale, combining management systems that create different stand properties might promote biodiversity due to complementary species assemblages. In European beech forests, nature conservation and policy advocate a mixture of unmanaged (UNM) forests and uneven‐aged (UEA) forests managed at fine spatial grain at the expense of traditionally managed even‐aged shelterwood forests (EA). Evidence that such a landscape composition enhances forest biodiversity is still missing. We studied the biodiversity (species richness 0D, Shannon diversity 1D, Simpson diversity 2D) of 14 taxonomic groups from bacteria to vertebrates in ‘virtual’ beech forest landscapes composed of varying shares of EA, UEA and UNM and investigated how γ‐diversity responds to landscape composition. Groups were sampled in the largest contiguous beech forest in Germany, where EA and UEA management date back nearly two centuries, while management was abandoned 20–70 years ago (UNM). We used a novel resampling approach that created all compositional combinations of management systems. Pure EA landscapes preserved a maximum of 97.5% γ‐multidiversity (0D, 1D) across all taxa. Pure and mixed UEA/UNM landscapes reduced γ‐multidiversity by up to 12.8% (1D). This effect was consistent for forest specialists (1D: −15.3%). We found only weak complementarity among management systems. Landscape composition significantly affected γ‐diversity of 6–9 individual taxa, depending on the weighting of species frequencies with strongest responses for spiders, beetles, vascular plants and birds. Most showed maximum diversity in pure EA landscapes. Birds benefited from UNM in EA‐dominated landscapes. Deadwood fungi showed highest diversity in UNM. Synthesis and applications. Our study shows that combining fine‐grained forest management and management abandonment at the landscape scale will reduce, rather than enhance, regional forest biodiversity. We found an even‐aged shelterwood management system alone operating at intermediate spatial scales and providing stands with high environmental heterogeneity was able to support regional biodiversity. However, some taxa require certain shares of uneven‐aged and unmanaged forests, emphasizing their general importance. We encourage using the here presented resampling approach to verify our results in forest landscapes of different composition and configuration across the temperate zone.

Tree, Shrub, and Grass Classification Using Only RGB Images

In this work, a semantic segmentation-based deep learning method, DeepLabV3+, is applied to classify three vegetation land covers, which are tree, shrub, and grass using only three band color (RGB) images. DeepLabV3+’s detection performance has been studied on low and high resolution datasets that both contain tree, shrub, and grass and some other land cover types. The two datasets are heavily imbalanced where shrub pixels are much fewer than tree and grass pixels. A simple weighting strategy known as median frequency weighting was incorporated into DeepLabV3+ to mitigate the data imbalance issue, which originally used uniform weights. The tree, shrub, grass classification performances are compared when all land cover types are included in the classification and also when classification is limited to the three vegetation classes with both uniform and median frequency weights. Among the three vegetation types, shrub is found to be the most challenging one to classify correctly whereas correct classification accuracy was highest for tree. It is observed that even though the median frequency weighting did not improve the overall accuracy, it resulted in better classification accuracy for the underrepresented classes such as shrub in our case and it also significantly increased the average class accuracy. The classification performance and computation time comparison of DeepLabV3+ with two other pixel-based classification methods on sampled pixels of the three vegetation classes showed that DeepLabV3+ achieves significantly higher accuracy than these methods with a trade-off for longer model training time.

Analysis of the Effect of a Series of Back Twist Blade Configurations for an Active Pitch-To-Stall Floating Offshore Wind Turbine

Abstract For a turbine mounted on a floating platform, extreme induced loads can be increased by up to 1.6 times those experienced by a turbine situated on a fixed base. If these loads cannot be reduced, towers must be strengthened which will result in increased costs and weight. These tower loads would be additionally exasperated for a pitch-to-feather controlled turbine by a phenomenon generally referred to as “negative damping,” if it were not avoided. Preventing negative damping from occurring on a pitch-to-feather controlled floating platform negatively affects rotor speed control and regulated power performance. However, minimizing the blade bending moment response can result in a reduction in the tower fore-aft moment response, which can increase the tower life. A variable-speed, variable pitch-to-stall (VSVP-S) floating semi-submersible wind turbine, which does not suffer from the negative damping and hence provides a more regulated power output, is presented. This incorporates a back twist blade profile such that the blade twist, starting at the root, initially twists toward stall and, at some pre-determined “initiation” point, changes direction to twist back toward feather until the tip. Wind frequency weighting was applied to the tower axial fatigue life trends of different blade profiles and a preferred blade back twist profile was identified. This had a back twist angle of −3 deg and started at 87.5% along the blade length and achieved a 5.1% increase in the tower fatigue life.

Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency–inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.

Cortisol levels and labor outcomes among women in the third trimester of pregnancy

This study aimed to analyze the relationship between cortisol levels and labor outcomes among women in the third trimester of pregnancy. The design of this study was the Longitudinal Study. Data collected included age, education, parity, birth space, quality of Antenatal care, Antenatal Care frequency, socioeconomic factors, cortisol levels, and birth weight. The laboratory test for cortisol using ELISA plasma method with the categories of Normal cortisol levels (5-23 micrograms per deciliter (mcg/dL) or 138-635 nanomoles per liter (nmol/L) and High cortisol levels (>23 micrograms per deciliter (mcg/dL) or >635 nanomoles per liter (nmol/L). The samples were 55 third trimester pregnant women who had Antenatal Care and gave birth in the same place. The data were collected for three months by the researcher. The data analyzed using Mann-Whitney Test. The results showed a p-value=0.000 (p<0.05) with the Mean Rank value at normal cortisol levels of 910.00, which was higher than the Mean Rank of high cortisol levels of 630.00. Thus, it can be concluded that there was a significant difference between the birth weight of babies with high cortisol levels and babies with normal cortisol levels among women in the third trimester of pregnancy with a different value of 27.5. Cortisol levels in the third trimester of pregnancy associated with low birth weight (LBW) incidence.

Research on Leakage Location of Spacecraft in Orbit Based on Frequency Weighting Matrix Beamforming Algorithm by Lamb Waves

Clashes between space debris and spacecraft in orbit may cause air leakages, which pose a substantial danger to the crew and the spacecraft. Lamb wave dispersion in spacecraft structures and the randomness of leak holes are the difficulties in leak location. To solve these problems, a frequency weighting matrix beamforming algorithm is proposed in this paper. The elastic Lamb waves that are caused by leakages are acquired by an ‘L’ shaped sensor array consisting of eight acoustic emission sensors. The angle of a leak can be obtained through the superposition of different time delays, and the intersection of two angles can be used to find the location of the leak. Traditional beamforming is improved by matching the wave speeds in different frequency bands and weightings according to the energy distribution. Narrowband filtering is used to delay overlay different signal speeds with different frequency bands via a dispersion curve. The weighting method is used to compensate the frequency band response of different leak holes. The detailed location algorithm process is introduced and verified by experiments. For 1.5 and 2 mm leak holes, location direction accuracies of 1.33° and 1.93° for one sensor array were obtained, respectively.

Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System.

Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.

Bag-of-Concepts representation for document classification based on automatic knowledge acquisition from probabilistic knowledge base

Text representation, a crucial step for text mining and natural language processing, concerns about transforming unstructured textual data into structured numerical vectors to support various machine learning and data mining algorithms. For document classification, one classical and commonly adopted text representation method is Bag-of-Words (BoW) model. BoW represents document as a fixed-length vector of terms, where each term dimension is a numerical value such as term frequency or tf-idf weight. However, BoW simply looks at surface form of words. It ignores the semantic, conceptual and contextual information of texts, and also suffers from high dimensionality and sparsity issues. To address the aforementioned issues, we propose a novel document representation scheme called Bag-of-Concepts (BoC), which automatically acquires useful conceptual knowledge from external knowledge base, then conceptualizes words and phrases in the document into higher level semantics (i.e. concepts) in a probabilistic manner, and eventually represents a document as a distributed vector in the learned concept space. By utilizing background knowledge from knowledge base, BoC representation is able to provide more semantic and conceptual information of texts, as well as better interpretability for human understanding. We also propose Bag-of-Concept-Clusters (BoCCl) model which clusters semantically similar concepts together and performs entity sense disambiguation to further improve BoC representation. In addition, we combine BoCCl and BoW representations using an attention mechanism to effectively utilize both concept-level and word-level information and achieve optimal performance for document classification.

Modeling of Individual HRTFs Based on Spatial Principal Component Analysis

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

UNIFYING ANALYSES OF MULTIPLE RESPONSES.

In dialectology we often encounter irreducible variation in its data, i.e., multiple responses to its probes about the form of a word or phrase. Dialectometry seeks to measure the differences between dialects and has developed several ways to measure the difference between responses when one or both of them is non-unique. We introduce here BILBAO DISTANCE, where the cardinality of response is unimportant, which may be combined with various weighting functions such as edit distance or inverse frequency weighting, and which yields intuitively appealing measures, e.g., when applied to a singleton set {a} and a set with the same element plus a second, yields d({a},{a,b}) = 0.5. It overcomes flaws in earlier proposals and is conceptually simpler and computationally more efficient to apply than earlier measures. We suspect that its results satisfy the metric axioms, as it is certainly symmetric and measures the difference between identical sets as zero.

Neural Network Based Hysteresis Compensation of Piezoelectric Stack Actuator Driven Active Control of Helicopter Vibration

Piezoelectric Stack Actuators (PSAs) have become promising actuators for active control of helicopter fuselage vibration owing to their large output force, rapid response speed, wide working frequency and light weight. However, the hysteresis nonlinearity of PSA has negative influence on the performance of PSA-driven active vibration control. In this paper, to improve the performance of PSA-driven active control of helicopter vibration, the hysteresis nonlinear neural network and hysteresis compensation neural network of PSA have been established based on the Nonlinear Auto Regression eXogenous (NARX) model. The PSA's voltage-displacement relations under two-harmonic actuations were used for training the neural networks of hysteresis nonlinearity and hysteresis compensation. Then the compensation neural network for PSA's hysteresis was integrated into an active control system for helicopter vibration. The results of experimental investigation performed on a scale model of a representative helicopter fuselage floor structure indicate that the neural network based hysteresis compensation of PSA for active control of helicopter vibration can effectively reduce vibration of the scaled frame structure and can reduce more vibration than without compensation.

DOSE-DEPENDENT RELATIONSHIP EFFECT OF PROBIOTIC VETOM 1 ON CUMULATIVE PARAMETERS OF JAPANESE QUAIL EGG PRODUCTION

The authors investigated the effect of probiotic Vetom 1 based on Bacillus subtilis strain DSM 32424 with concentration of active component 106 CFU/g on cumulative indices of Japanese quail egg-laying. The specimen was applied in doses of 50, 75 and 100 mg/kg of the live weight of quails. The probiotic Vetom 1 increases the number of ovulating birds. The maximum increase in this indicator was observed when using Vetom 1 dosed as 50 mg/kg of the body weight. The egg mass variability depends completely on the dose. Being used in a dose of 50 mg/kg weight, a higher variability of the egg mass was observed. This pattern has not been recorded in the maximum studied dose. The cumulative function in terms of eggs in unit and mass equivalents is slightly expressed in the initial period when applying Vetom 1. In the final period of specimen applying, the highest accumulation is observed when using Vetom 1 in a dose of 50 mg/kg of mass. When applying Vetom 1 in doses of 75 and 100 mg/kg, a direct correlation between ovulation frequency and egg weight was observed only in the initial period of the specimen applying, and the dose of 50 mg/kg - only for the eighth day of it.

Design of Noise Testing System for Airborne Electromechanical Equipment

Noise test system model of current mechanical and electrical equipment is backward, which is easy to cause low test precision and long time-consuming problems. The afterward noise reduction work cannot be carried out smoothly. For this reason, a new noise test system for airborne electromechanical equipment is designed. The system is based on FPGA and MCU, including the system of power supply, speed sensor, noise sensor and so on. According to the noise measurement algorithm formulated by the International Electrotechnical Commission (IEC), the calculation of frequency and time weights in noise measurement is digitized. The digital processing algorithm of 1/3 octave spectrum analysis is combined to realize the noise test of airborne electromechanical equipment. The experimental results show that the designed system has strong anti-interference, high accuracy of noise testing, fast testing speed and conducive to the implementation of noise reduction work in the later period.

Определение частоты основного тона методом поиска пиков в амплитудном спектре с добавлением весовых частотных функций

Определение частоты основного тона методом поиска пиков в амплитудном спектре с добавлением весовых частотных функций

Landslide susceptibility mapping along national highway-1 in Jammu and Kashmir State (India)

A remote sensing and GIS-based landslide susceptibility mapping (LSM) has been carried out using frequency ratio (FR) and weight of evidence (WoE) methods to identify and delineate the potential failure zones along National Highway – 1. The thematic layers of various landslide causative factors have been generated for modeling in GIS. In addition, a landslide inventory along the road network was prepared using satellite imagery, Google earth, and extensive field visits. LSM classified the area into five susceptibility classes: very low, low, moderate, high, and very high classes. The validation result further substantiates the study and inferred that area under success rate curve shows a satisfactory relation between landslide affecting factors and landslide occurrences in case of FR and WoE models having an accuracy of 86.57% and 76.86%, respectively. A landslide density method has also been adopted for validation of LSM which showed acceptable results with decreasing trend of landslide density from very high to very low susceptible zone for both FR and WoE models. The LSM generated will be helpful for various stakeholders like planners, engineers, designers, and local public for future construction and maintenance in the study area.