Search

This study presents the hierarchical structure of 50 sub-criteria divided into 7 main criteria for the assessment of electric vehicle (EV) deployment. Two options, Average Rank Transformations and Analytic Hierarchy Process methods, were applied in determining the local weights of the sub-criteria. The sufficient compatibility of expert opinions was accomplished using the averages of the ranks of the main criteria and sub-criteria as the result of solving the problem. The averages of the local weights were calculated employing three Multiple Criteria Decision-Making methods that increased the reliability of the research results. Based on this, the global weights and priorities of the sub-criteria were evaluated. The experts suppose that EV deployment at the national level is mainly affected by the higher cost of manufacturing and purchasing EVs, the application of financial incentives for purchasing EVs, the lack of exhausted gasses, the installation of fast charging points, and the absence of infrastructure in the five largest cities nationwide. The obtained results demonstrate that out of 50 sub-criteria, the cumulative global weight of the 10 most important sub-criteria (mainly based in economics) amounts to more than 35%, whereas that of the 22 most important sub-criteria have a weight above the average (0.2), reaching approximately 65%. The findings can be put into practice by state decision makers of EV deployment.

Height and weight measuring devices simultaneously provide ideal weight information that will be handy for users. This study aims to provide an instrument that can be used as a measuring instrument for height and weight and whether or not a person's body is ideal. Therefore, in research, measuring instruments that can simultaneously measure height and weight and provide ideal information or not measured weight. This measuring instrument uses Arduino Uno as its brain, ultrasonic sensors to measure height, and strain gauge/load cell sensors to measure weight. Arduino processes data from both sensors to get the ideal body weight (BBI). The height, weight, and ideal body criteria values will be displayed on the LCD. Furthermore, the speaker will issue sound information regarding the condition of body weight, namely ideal, fat, or thin. Based on the testing and data analysis results, the average error percentage value in height measurement is 0.45%, and in weight measurement is 1.65%. At the same time, the success rate of the appearance of voice information is 99%. [ABSTRACT FROM AUTHOR]

Some composite estimators based on various combinations of two different existing estimators are obtained for domain estimation. The estimation of weights and thus obtaining optimum weights to combine two or more different existing direct and indirect estimators to form composite estimators are not an easy task for practitioners due to many reasons. To account for the absence of optimum weights, we obtained the sensitivity performance intervals for weights with respect to the proposed composite estimator. Subsequently, we determined the sensible values of the involved weights. The aim of this procedure was to confine the superiority for different composite combinations i.e., simple direct vs. direct ratio, simple direct vs. synthetic ratio and direct ratio vs. synthetic ratio composite estimators as compared to the existing estimators. [ABSTRACT FROM AUTHOR]

Industrial greenhouses have automated control systems for climate, lighting, irrigation, ventilation, and heating regulation using different types of feedback sensors. Nowadays it is a trend to increase the data precision and measurement data amount, thus various additional IoT sensors are installed, and the regulation becomes more precise, due to available data, which enables new analytical features to create new control rules or strategies. The general aim is to raise the level of process automation, quality, energy efficiency, and other important parameters. Still, further, we go into data resolution and amount, and the problem of data reliability and interpretation starts to become a challenging problem. In this article, authors focus on earlier developed PAR sensor modules and continuous tomato crop weight sensor modules (TWS) testing and received data analysis from an industrial greenhouse. Both sensors were tested in detail at the tomato greenhouse of 'Latgales Darzenu Logistika' in Mezvidi parish, with a total growing area of 5,062.4 m² from 1.05.2022 to 30.06.2022., and gathered data is analysed for this period. Received sensor data can be used as the main feedback signal to create a lighting control strategy, same time increasing energy efficiency and reducing also costs. As artificial lighting energy consumption costs make 20-40% of total greenhouse costs, it is worth having a more precise lighting control system algorithm, integrating the crop growth increase and accumulated light energy during the day from the sun, and then adding only the missing amount (also period) of light provided by artificial lighting. Experimental studies of both sensor data, show that plants reaction can be monitored, as by decreasing the lighting period and temperature setpoint by 6% each, the plants daily weight gain decreases by 14%, and it can be measured already in first day after the new settings were set in place. [ABSTRACT FROM AUTHOR]

Copyright of Nutrition & Feed Technology Journal / Jurnal Ilmu Nutrisi dan Teknologi Pakan is the property of IPB University 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.)

One of the world's fastest-growing food industries is aquaculture. As a result, aquaculture is a successful industry. Lack of care in the preparation of fish feed is a problem for this industry because it leads to the production of low-quality livestock.This study uses the Arduino microcontroller and ESP 8266 to create an automatic fish feeder that can be remotely controlled using a smartphone. Setting the fish's feeding routine is one way to exercise control. The test method used is the experimental method by conducting 5 times the scheduling time test and 10 times the feed weight measurement test.The test results shows that the tool can control a more regular fish feeding schedule. The percentage accuracy of the automatic fish feed scheduling test reaches 100%, while the percentage accuracy of the feed weight measurement test reaches 90%. [ABSTRACT FROM AUTHOR]

This paper constructs an energy model based on local features used in stereo matching. The local features include the similarity between different image areas, the matching cost function pattern, the connection between neighbor pixels, and the occlusion geometric relationship. Based on these features, we define the weight of each data term and smoothing term in the energy function and then design an algorithm to solve the energy model and get disparity results. The significant improvements of this paper include as following. 1) We modify the structure of the energy function. First, we define the weight of the data term based on the reliability of its corresponding disparity result, which is obtained by cost function features and the occlusion geometric relationship. Then we define the weight of the smoothing term by analyzing the characteristic relation between neighbor super-pixels. We can also reduce the computational complexity by detecting and reducing some low-strength connections. 2) We proposed an algorithm based on pairwise Markov random field (MRF) (Taniai et al., IEEE Trans Pattern Anal Machine Intell 40(11): 2725–2739, 2017) and local greedy iteratively, which can be used to solve the energy model. 3) In post-optimation, we select some areas with severe occlusion and fewer matching clues for post-interpolation fitting to optimize the results. The experiment shows that the proposed method reduced the average percentage of bad pixels (in bad 3) to 6.06 on the Middlebury dataset and 1.42 on the KITTI dataset. Finally, we compare our results with those of MC-Cnn (Zbontar and LeCun 2015), CF-Net (Shen et al., 2021), Guided-Stereo (Poggi et al., 2019), Gwc-Net (Guo et al., 2019) and Patchmatch-Net(PM-Net) (Wang et al., 2021) to verify the improved speed and accuracy of our algorithm, especially at recognizing the depth of changing edges and small objects. This paper's relevant research can contribute to practical engineering practices such as assisted vision, intelligent driving, and robot grasping control. [ABSTRACT FROM AUTHOR]

This paper addresses the question of how the 'weight' or 'strength' of normative reasons is best understood. We argue that, given our preferred analysis of reasons as sources of normative support, this question has a straightforward answer: the weight of a normative reason is simply a matter of how much support it provides. We also critically discuss several competing views of reasons and their weight. These include views which take reasons to be normatively fundamental, views which analyze reasons as evidence concerning what one ought to do, views which analyze reasons in terms of good reasoning, and views which analyze reasons as explanations of ought-facts, fittingness-facts, or goodness-facts. [ABSTRACT FROM AUTHOR]

This letter proposes an efficient infrared small target detection method based on the 1-D difference of guided filtering (DoGF). First, the 1-D DoGF is constructed by measuring the difference of image structure fidelity between primitive guided filtering (GF) and local variance weighted GF from the perspective of 1-D signal analysis, which can effectively filter out 1-D noise components and protrude pulse signals. Second, the 1-D row and column DoGF are applied to process the infrared image along horizontal and vertical directions, respectively, and then the row–column crossed DoGF (rcDoGF) and column–row crossed DoGF (crDoGF) are calculated and integrated, which can greatly highlight the pulse-like small target signal and eliminate the background clutter. Finally, the small targets can be extracted with a simple adaptive threshold. Experimental results show that the proposed algorithm has high detection accuracy for small infrared targets under heavy noise interference, as well as for targets with different sizes and shapes. [ABSTRACT FROM AUTHOR]

This paper presents the results from 92 fire load density surveys conducted in 52 office buildings of Pakistan. The combination method of surveying that includes both inventory and weighing methods is used to determine the fire load of 92 office rooms, including 44 private and 48 government offices. Multiple linear regression analysis techniques are applied to assess the relationship of Fire Load Density (FLD) with variables according to the characteristics of the office rooms, such as office type, category, combustible materials, room dimensions, and ventilation conditions. Probabilistic models for FLD are developed using the regression analysis of the survey data. The survey data is further used to determine the maximum fire intensity in office buildings in Pakistan. The survey results show that the FLD increases with the increase in the area of the office. The percentage of wood is found to be the most contributing factor in the fire load. It has been noted that the fire load values are different for government and private offices, whereas the Building Code of Pakistan (BCP) has the same value for both. Statistical results presented in this study will be helpful in the fire safety and fire-resistant structure design of buildings in Pakistan. [ABSTRACT FROM AUTHOR]

Determining the slaughter weight of pigs is crucial to the profitability of swine production farms. Unfortunately, in developing countries, the basic infrastructure for weight measurement may not always be available, affecting farmers' income. This study presents a machine learning-based approach to determine the dressed weight of pigs using four morphometric dimensions: paunch girth (PG), heart girth (HG), body length and wither height, which can be measured in situ. Different neural network model structures were constructed taking LM, GDX and BR training algorithms, tansigmoid/logsigmoid hidden layer transfer functions and 5–30 hidden layer neurons (HLNs). Results showed that LM training algorithm with logsigmoidal transfer function and 20 HLNs resulted in 99.8% accuracy in determining the pig dressed weight. Further, the number of morphometric parameters as inputs was gradually reduced and it was found that 99% accuracy can still be achieved using just PG and HG, thereby reducing the measurement time. [ABSTRACT FROM AUTHOR]

Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding of basal processes and quantifying uncertainty in sea level rise projections with respect to topographic uncertainty. This can be achieved with sequential Gaussian simulation (SGS), which is used to generate multiple nonunique realizations of geological phenomena that sample the uncertainty space. However, SGS is very CPU intensive, with a computational complexity of O($\boldsymbol{N}\boldsymbol{k}$Nk3), where $N$N is the number of grid cells to simulate, and $\boldsymbol{k}$k is the number of neighboring points used for conditioning. This complexity makes SGS prohibitively time-consuming to implement at ice sheet scales or fine resolutions. To reduce the time cost, we implement and test a multiprocess version of SGS using Python's multiprocessing module. By parallelizing the calculation of the weight parameters used in SGS, we achieve a speedup of 9.5 running on 16 processors for an $\boldsymbol{N}$N of 128,097. This speedup—as well as the speedup from using multiple processors—increases with $\boldsymbol{N}$N. This speed improvement makes SGS viable for large-scale topography mapping and ensemble ice sheet modeling. Additionally, we have made our code repository and user tutorials publicly available (GitHub and Zenodo) so that others can use our multiprocess implementation of SGS on different datasets. [ABSTRACT FROM AUTHOR]

To obtain higher economic benefits, large eel breeding companies classify live eels by weight. Due to their strong mobility and smooth body surface, living eels are not suitable for traditional mechanical weight measurement. In this study, a live eel sorting machine based on machine vision was developed, and a novel method was developed for obtaining live eel weight measurements through images. First, a backlit workbench was designed to capture static images of eels, and then the projection area and skeleton length of the images were obtained by image preprocessing. For the eel's body shape, which is generally cylindrical and gradually transitions to a flat tail, the tail posture changes affect the shape of the images; thus, a weight measurement model combining the projected area and the skeleton length was proposed. The optimal scale division coefficient of the weight model was found to be 0.745 by experimentation. Then, select eels of different weight ranges were used for model error verification and to obtain the correction function of the error. The weight gradient was used to confirm the corrected eel weight model. Finally, the system calculation results were compared with the actual measurement results. The root mean square error (RMSE) was 12.94 g, and the mean absolute percentage error (MAPE) was 2.12%. The results show that the proposed method provided a convenient, fast, and low-cost non-contact weight measurement method for live eels, reduced the damage rate of live eels, and can meet the technical requirements of actual production. [ABSTRACT FROM AUTHOR]

Yarn linear density and linear density unevenness between fragments involve the mass and mass variation of yarn per unit length, which are important indices to reflect the uniformity of yarn thickness. Aiming at the shortcomings of the traditional testing method, which uses a yarn length tester to test these indices by counting length first and then cutting and measuring weight, a testing device that measures weight and counts length synchronously is designed and developed, so that the yarn can be continuous, recyclable, and reusable. Length counting is conducted by a length-counting disk connected to a photoelectric coded disk, and the result of length counting can be accurate to 0.01 m. The original skein frame with a perimeter of 1 m is replaced, so that the error caused by yarn overlapping is avoided. Through program control, the testing of a plurality of groups of linear density values can be completed at one time to calculate the linear density unevenness of different fragments of yarn, and the yarn can be led to a recovery spool through a yarn guide cylinder to form a new package. Polyester/viscose 65/35 blended yarn was taken as a test sample and subjected to statistical analysis using SPSS software. It is found that the results of the newly developed device are closer to the arbitration value; the whole experiment is completed at one time, which avoids the secondary error and reduces the labor intensity; and the raw materials can be recycled, which saves labor and raw material costs. The device has high value for industrialization and popularization. [ABSTRACT FROM AUTHOR]

This article presents an improved method of measuring technology similarity by introducing a subject-action-object (SAO) analysis that uses the feature weights of semantic structure and professional vocabulary to measure technology similarity in the medical field. First, the SAO semantic structures are extracted and cleaned; then the structures related to technology are identified using a semantic network of the unified medical language system (UMLS). Second, the similarity between the SAO semantic structures is evaluated using semantic information from the Metathesaurus of the UMLS. Third, the feature weights of the SAO semantic structure are introduced to represent the importance of the patentees’ technology features. Finally, using the SAO and weight information, each patentee's vector is constructed to measure the technology complementarity between different patentees. This study conducts empirical research on Alzheimer's disease. The results indicate that the propose method for measuring technology similarity enables finer distinctions with more reliable outcomes than the traditional methods that are based on keywords and international patent classification. [ABSTRACT FROM AUTHOR]

Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional operations. Their goal is to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory efficient. Meeting both these requirements remains a challenge. To this end, we propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling kernels that are based on the exponent of the Dice-Sørensen coefficient and the exponential maximum, respectively. AdaPool improves the preservation of detail on a range of tasks including image and video classification and object detection. A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, the learned weights can also be used to upsample activation maps. We term this method adaUnPool. We evaluate adaUnPool on image and video super-resolution and frame interpolation. For benchmarking, we introduce Inter4K, a novel high-quality, high frame-rate video dataset. Our experiments demonstrate that adaPool systematically achieves better results across tasks and backbones, while introducing a minor additional computational and memory overhead. [ABSTRACT FROM AUTHOR]

Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions; 2) malicious attacks, including conventional and computation-based attack strategies; 3) robustness estimation methods using either analytical approximation or machine learning-based prediction; 4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-third of epileptic patients experience seizures attack even with medicated treatment. The menace of SUDEP (Sudden unexpected death in epilepsy) in an adult epileptic patient is approximately 8–17% more and 34% in a children epileptic patient. The expert neurologist manually analyses the Electroencephalogram (EEG) signals for epilepsy diagnosis. The non-stationary and complex nature of EEG signals this task more error-prone, time-consuming and even expensive. Hence, it is essential to develop automatic epilepsy detection techniques to ensure an appropriate identification and treatment of this disease. Nowadays, graph-theory has been considered as a prominent approach in the neuroscience field. The network-based approach characterizes a hidden sight of brain activity and brain-behavior mapping. The graph-theory not even helps to understand the underlying dynamics of EEG signals at microscopic, mesoscopic, and macroscopic level but also provide the correlation among them. This paper provides a review report about graph-theory based automated epilepsy detection methods. Furthermore, it will assist the expert's neurologist and researchers with the information of complex network-based epilepsy detection and aid the technician for developing an intelligent system that improving the diagnosis of epilepsy disorder. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates. [ABSTRACT FROM AUTHOR]

Waste electrical and electronics equipment (WEEE) recyclers have become a boon for countries as they assist to reduce carbon emissions by recycling the WEEE in the most ecofriendly way. The evaluation and choice of optimal WEEE recycling partner is a significant and multifaceted decision for the managerial experts because of the involvement of several qualitative and quantitative criteria. Thus, the aim of the article is to propose a novel methodology by integrating a combined compromise solution approach and similarity measure within the context of single-valued neutrosophic sets (SVNSs) and, then, used to solve the decision-making problem. In this approach, the criteria weights are evaluated by a new procedure based on a similarity measure. For this, a novel similarity measure is introduced for SVNSs and also presented its efficiency over existing similarity measures. To investigate the efficiency and practicality of the introduced approach, a case study of WEEE recycling partner selection is taken under the SVNSs environment. Finally, comparison and sensitivity analysis are made to check the robustness and stability of the developed methodology. The outcomes of this article exemplify that the developed approach is more suitable and well consistent with existing approaches. [ABSTRACT FROM AUTHOR]

Graph learning has emerged as a promising technique for multi-view clustering due to its efficiency of learning a unified graph from multiple views. Previous multi-view graph learning methods mainly try to exploit the multi-view consistency to boost learning performance. However, these methods ignore the prevalent multi-view diversity which may be induced by noise, corruptions, or even view-specific attributes. In this paper, we propose to simultaneously and explicitly leverage the multi-view consistency and the multi-view diversity in a unified framework. The consistent parts are further fused to our target graph with a clear clustering structure, on which the cluster label to each instance can be directly allocated without any postprocessing such as $k$ k -means in classical spectral clustering. In addition, our model can automatically assign suitable weight for each view based on its clustering capacity. By leveraging the subtasks of measuring the diversity of graphs, integrating the consistent parts with automatically learned weights, and allocating cluster label to each instance in a joint framework, each subtask can be alternately boosted by utilizing the results of the others towards an overall optimal solution. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of our model in comparison to several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts are independent. This is reflected in the setting of preferences and weights. Nevertheless, each expert’s role is played through communication, collaboration, and cooperation with other individuals. The interaction from others may affect the power of an expert as well as his/her opinion. Furthermore, it is noted that a link path with the highest degree of trust is the most efficient information transmission channel. Inspired by these findings, an optimal trust-induced consensus process is designed with the usage of intuitionistic fuzzy preference relation. The comprehensive weight of each expert is decomposed into two portions, namely: 1) the individual weights and 2) interactive weights. Three optimization models are constructed to achieve weight parameters under different decision situations, where the weight parameters are represented through a 2-order additive fuzzy measure and the Shapley value. To reflect the interaction, the Choquet integral is employed for aggregating opinions, and a novel distance measure is adopted for accomplishing a consensus index. An illustrative example and comparison are put in practice to show the effectiveness and improvements of the proposed method. [ABSTRACT FROM AUTHOR]

The generation of collective preference assessments occupies a critical position in deriving accurate and reliable alternative rankings in the context of large-scale group decision making (LSGDM). In general, the collective opinion generation framework entails the following three phases, which are clustering analysis, weighting clusters, and preference aggregation. However, the clustering of experts has been frequently based on preference similarities among them without taking into account individual opinions in which knowledge elicitation plays a crucial role. The traditional collective opinion generation framework suffering from this drawback may result in unreliable decision outcomes. To this end, we propose an expertise-structure and risk-appetite-integrated two-tiered collective opinion generation framework to address this concern. The first tier of the two-tiered collective opinion generation framework divides the entire expert group into several subgroups based on individual expertise structures, which are extracted from hesitant fuzzy linguistic term set (HFLTS)-based preference assessments, and it then weighs the resulting clusters in accordance with the overall expertise levels. The second-tier clusters the first-tier subgroups conditioned on the indicator of individual assessment similarities and gathers the generated subgroup preference constructs in the use of the risk appetite-oriented power average operator. In addition, the notion of proportional HFLTSs was introduced to manifest collective evaluations in second-tier subgroups to eliminate information loss and distortion. The effectiveness and flexibility of the proposed collective opinion generation algorithm are eventually illustrated by a case study and a comparison analysis. [ABSTRACT FROM AUTHOR]

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of typical kernels, e.g., Gaussian kernels, arc-cosine kernels, are fully symmetric, we leverage a numerical integration technique, deterministic fully symmetric interpolatory rules, to efficiently compute quadrature nodes and associated weights for kernel approximation. Thanks to the full symmetric property, the applied interpolatory rules are able to reduce the number of needed nodes while retaining a high approximation accuracy. Further, we randomize the above deterministic rules by the classical Monte-Carlo sampling and control variates techniques with two merits: 1) The proposed stochastic rules make the dimension of the feature mapping flexibly varying, such that we can control the discrepancy between the original and approximate kernels by tuning the dimnension. 2) Our stochastic rules have nice statistical properties of unbiasedness and variance reduction. In addition, we elucidate the relationship between our deterministic/stochastic interpolatory rules and current typical quadrature based rules for kernel approximation, thereby unifying these methods under our framework. Experimental results on several benchmark datasets show that our methods compare favorably with other representative kernel approximation based methods. [ABSTRACT FROM AUTHOR]

This article investigates multiplicative consistency ascertaining, inconsistency repairing, and weights derivation for hesitant multiplicative preference relations (HMPRs). First, the completely multiplicative consistency and weakly multiplicative consistency of HMPRs are defined. Based on them, 0–1 mixed programming models and simple algebraic operations are proposed to ascertain the multiplicative consistency of HMPRs. Then, some goal programming models are developed to generate the weights from consistent HMPRs and to revise inconsistent HMPRs. An integrated procedure to manage the multiplicative consistencies of HMPRs is designed. The proposed methods are also extended to accommodate incomplete HMPRs, and to estimate missing values. Finally, some numerical examples, a comparative analysis with existent approaches, and a simulation analysis are included to illustrate the practicality and effectiveness of the developed models. [ABSTRACT FROM AUTHOR]

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery. [ABSTRACT FROM AUTHOR]

This study evaluated the physicochemical and antibacterial properties of EQUIATM coat liquid (E) after incorporation of zinc oxide (ZnO) and titanium dioxide (TiO2) nanoparticles. ZnO and TiO2 (1 wt.% and 2 wt.%) were dispersed in EQUIA coat. Principal component analysis (PCA) and cluster analysis were performed to visualize systemic variation. Antibacterial activity was evaluated by colony-forming units and crystal violet staining using Streptococcusmutans and Lactobacillusacidophilus after 24 h, 48 h, and 72 h, and the microstructure was studied by scanning electron microscopy. The weight change was analyzed at 1 and 21 days. The PCA for TiO2- and ZnO-based groups showed 100% variance at all spectral ranges at 600–800/cm and 800–1200/cm, whereas 1200–1800/cm and 2700–3800/cm spectral regions demonstrated 99% variance. The absorbance values were significant (p < 0.05) for both nanoparticles-based adhesives, and the specimens with 2 wt.% ZnO showed the maximum response by minimum bacterial attachment, and the control group showed the least response by maximum attachment. The weight change percentage was reduced after the incorporation of antibacterial nanoparticles. It is suggested that EQUIATM coat containing nanoparticles exhibits promising results, and it may be recommended to clinically use as an improved coating material. [ABSTRACT FROM AUTHOR]

Yarn linear density and linear density unevenness between fragments involve the mass and mass variation of yarn per unit length, which are important indices to reflect the uniformity of yarn thickness. Aiming at the shortcomings of the traditional testing method, which uses a yarn length tester to test these indices by counting length first and then cutting and measuring weight, a testing device that measures weight and counts length synchronously is designed and developed, so that the yarn can be continuous, recyclable, and reusable. Length counting is conducted by a length-counting disk connected to a photoelectric coded disk, and the result of length counting can be accurate to 0.01 m. The original skein frame with a perimeter of 1 m is replaced, so that the error caused by yarn overlapping is avoided. Through program control, the testing of a plurality of groups of linear density values can be completed at one time to calculate the linear density unevenness of different fragments of yarn, and the yarn can be led to a recovery spool through a yarn guide cylinder to form a new package. Polyester/viscose 65/35 blended yarn was taken as a test sample and subjected to statistical analysis using SPSS software. It is found that the results of the newly developed device are closer to the arbitration value; the whole experiment is completed at one time, which avoids the secondary error and reduces the labor intensity; and the raw materials can be recycled, which saves labor and raw material costs. The device has high value for industrialization and popularization.

At present, the weighing process of concrete aggregate is cumbersome, and the weighing result has a large error. This paper proposes a method for measuring the weight of irregular bone stockpiles based on binocular vision. Firstly, based on the principle of binocular vision, the improved SMG stereo matching algorithm and OTSU threshold segmentation algorithm are used to calculate the 3D point cloud coordinates corresponding to the disparity map. Then, the 3D reconstruction of the aggregate pile and the calculation method of volume and mass are proposed. The results show that the method can accurately measure the aggregate pile weight, and the measurement error is less than 6%. The method can effectively reduce the operating cost of the concrete batching station and improve the measurement efficiency. [ABSTRACT FROM AUTHOR]

State estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) are subject to catastrophic forgetting when learning stream of data. Inspired by the knowledge of neuroscience, this article develops a memory formation system (MFS) to establish memory for GANs. MFS is composed of three modules, including the identifier, weights upgrade (WU), and weights reactivate (WR). These modules simulate the function of encoding, consolidating, and retrieving in memory formation of human. Identifier creates indexes to label continuous tasks and these indexes are used as a cue when the corresponding tasks are recalled. WU and WR work as synaptic consolidation and system consolidation, respectively. In WU, a novel method, weight saliency measure (WSM) is proposed to measure the saliency of weight. Valuable weights are protected and invaluable weights are released for update when GAN is trained for the new task. In WR, traditional data-replay methods are improved by selecting ${k}$ representatives in each class and their feature vectors are restored. When pseudo data of this class are regenerated, only the samples with distance less than a threshold can be used in future training. Experimental results based on the testing of continual image generation and continual 3-D reconstruction show that MFS can establish a memory system to handle the catastrophic forgetting problem effectively. [ABSTRACT FROM AUTHOR]

The Chinese BeiDou Navigation Satellite System (BDS-3) has completed the network construction and begun to provide high-precision Positioning, Navigation, and Timing (PNT) services for users all over the world. In this paper, we study the Precision Point Positioning (PPP) time comparison based on the BDS-3 new signal. The data presented in this paper includes the observation data from the National Time Service Center of Chinese Academy of the Sciences (NTSC) and the Electronics Academy of Sciences in Czech Republic (TP), and the precise product data released by the International GNSS Service (IGS), which includes the high-precision clock and orbit products. The results show that the multipath noise of the BDS-3 new signal (B1C and B2a) is less than that of the Asia-Pacific region BDS-2 (B1I and B2I), and the standard deviation (STD) of the multipath noise of B2a is the least. When the BDS-3 PPP is applied to the common clock short-baseline time comparison, the standard deviations of the BDS-3 and BDS-2 signals are 0.0296 ns and 0.0341 ns, respectively, the standard deviation of BDS-3 is smaller than that of BDS-2, and therefore the short-baseline time comparison performance of BDS-3 PPP is better than that of BDS-2, the standard deviation of BDS-3 PPP has increased by 13.19% compared with BDS-2. For long-baseline time comparisons between UTC (NTSC) and UTC (TP), the results based on the BDS-3 single system, or BDS-3 with other navigation system fusion PPP time comparison, follow the same trend as the results released by the International Bureau of Weights and Measures (BIPM). The residual standard deviations relative to the BIPM are 0.7039 ns and 0.2994 ns respectively. The stability of the BDS-3 new signal PPP and multi-GNSS fusion PPP time comparison has the same magnitude as the BIPM. As a result, with the completion of BDS-3, the high-precision time comparison based on BDS-3 expected to offer better services for users. The experimental results demonstrate that BDS-3 could be used to calculate the Coordinated Universal Time (UTC) in the future. [ABSTRACT FROM AUTHOR]

This article proposes the problem of joint state estimation and correlation identification for data fusion with unknown and time-varying correlation under the Bayesian learning framework. The considered data correlation is represented by the randomly weighted sum of positive semi-definite matrices, where the random weights depict at least three kinds of unknown correlation across single-sensor measurement components, multisensor measurements, and local estimates. Based on the variational Bayesian mechanism, the joint posterior distribution of the state and weights is derived in a closed-form iterative manner, through minimizing the Kullback–Leibler divergence. The three-case simulation shows the superiority of the proposed method in the root-mean-square error of estimation and identification. [ABSTRACT FROM AUTHOR]

Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that the introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that the incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy STUDENT’S t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this article, we make two significant contributions. The first is that both the narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under STUDENT’S t-distribution ensures that spatial information is introduced into the updated parameters of FRSC, which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information, and provides better segmentation results than state-of-the-art clustering algorithms. [ABSTRACT FROM AUTHOR]

The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e., nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed algorithm in comparison with the existing counterparts. [ABSTRACT FROM AUTHOR]

With the rapid development of Android devices, Android is currently one of the most popular mobile operating systems. However, it is also believed to be an entry point of many attack vectors. The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly elusive. Therefore, we propose a novel feature selection algorithm called frequency differential selection (FDS) and weight measurement for Android malware detection. The purpose is to solve the shortcomings of the existing feature selection algorithms in detection and to filter out other effective features. Weight measurement is used to optimize the detection accuracy of the classifier and improve the accuracy of detection. We combine the optimized features and the detection model for verification and evaluation. Experiments were conducted on the OmniDroid dataset, which is a large and comprehensive dataset of features extracted from 22,000 real malware and benign samples. Theoretical analysis and experimental results showed that the FDS algorithm and weight measurement are effective, feasible, and exhibit advantages over other existing malware detection models. In detecting Android malware samples, the proposed method can achieve an accuracy of 99% and an F1-score of 98%. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of computing the approximate preinverses of a fuzzy matrix with max-product composition. Employing the weighted $L_1$ distance, a variety of evaluation functions are defined according to the preselected weights, and then, an analytical method is proposed to determine all approximate preinverses of equal quality by minimizing the preselected evaluation function. Since only the approximate preinverses with fewer or preferably no zeros are acceptable for many practical applications, a criterion for selecting weights is established to ensure that the determined approximate preinverses are desirable. Some examples are given to illustrate our results. [ABSTRACT FROM AUTHOR]

Fuzzy sets and fuzzy rough sets are widely applied in data analysis, data mining, and decision-making. So far, the common method is to use rough approximate operators to induce aggregation functions when fuzzy rough sets are used for multicriteria decision-making (MCDM). However, they are parametric linear and the corresponding weights are additive measures. In this article, we give a novel method for MCDM based on fuzzy covering rough sets by using the nonadditive measure [i.e., fuzzy measure (FM)] and the nonlinear integral [i.e., Choquet integral (ChI)]. First, two nonadditive measures are presented by fuzzy covering lower and upper approximation operators, respectively. Moreover, both of them are FMs which are called $\beta$ -neighborhood approximation measures. Second, two types of ChIs with respect to $\beta$ -neighborhood approximation measures are constructed. A novel method, which considers the association, is presented to solve the problem of MCDM under the fuzzy covering rough set model. Third, a new approach based on $\beta$ -neighborhood approximation measures is proposed for attribute reductions in a fuzzy $\beta$ -covering information table. This approach of attribute reductions is used in MCDM. Finally, both new methods above are compared with other methods through some numerical examples and UCI datasets, respectively. [ABSTRACT FROM AUTHOR]

Linguistic multiple attribute decision making (LMADM) has been widely used in different decision contexts to achieve a final solution, e.g., obtaining the ranking of alternatives. Evaluation information using linguistic scales in LMADM is usually expressed in simple discrete linguistic terms. In this article, we consider that each simple linguistic term in LMADM actually has a continuous representation (denoted as a linguistic two tuples) whose rounding operation matches the linguistic term. Under such conditions, the ranking of alternatives based on the simple linguistic terms may not be consistent with that based on the linguistic two tuples. Thus, this article studies the linguistic scale ranking consistency issue in LMADM under seven classical and commonly used decision rules: weighted averaging, ordered weighted averaging, weighted geometric averaging, ordered weighted geometric averaging, preference ranking organization method for enrichment evaluation, technique for order preference by similarity to an ideal solution, and elimination et choice translating reality. We first define the concept of the linguistic scale ranking consistency in LMADM. Afterward, several consistency conditions are presented analytically for the selected decision rules to guarantee the linguistic scale ranking consistency. Finally, we present the detailed theoretical and simulation-based comparisons. The theoretical comparisons show the roles of decision rules and granularity in the consistency conditions, and the simulation-based comparisons demonstrate the performance of the selected decision rules in defending against the ranking inconsistency in LMADM. [ABSTRACT FROM AUTHOR]

An event-triggered consensus filter is proposed in this letter for state estimation in distributed sensor networks based on the hybrid consensus on measurement and consensus on information scheme. For bandwidth reduction and energy saving, an event-triggered transmission strategy is developed in which each node selectively transmits only the most relevant data so as to reduce data transmission while preserving the filtering performance. Two different transmission tests are performed in parallel, respectively on the prior and on the likelihood information pair, to evaluate the information loss (measured in terms of Kullback-Leibler divergence) that would be incurred if the current values were replaced by the predicted ones according to the last transmitted data. Simulation results on a distributed target tracking case-study demonstrate outperformance of the proposed filter with respect to conventional triggered filters. [ABSTRACT FROM AUTHOR]

In this letter, we address the multistatic object localization problem when the transmitter position and signal propagation speed are not known. By transforming the time delay measurement model, we first formulate a non-convex weighted least squares problem, which is then relaxed into a convex semidefinite programming (SDP) problem by applying semidefinite relaxation. It turns out that the relaxed SDP problem is too loose and fails to achieve the Cramer-Rao lower bound (CRLB) performance. To address this problem, we propose to tighten the relaxed SDP problem by adding a second-order cone constraint to reach better solutions. Simulation results show that the proposed method is able to achieve the CRLB. [ABSTRACT FROM AUTHOR]

The problem of distributed constraint-coupled estimation for multi-subsystem is addressed, in which there exist coupled linear equality constraints generated by cooperating neighbor subsystems (nodes). Our goal is to design a distributed estimator such that the state estimates obtained by each node can satisfy the all/global constraints based on the fact that each node only knows the local coupling constraints with its neighbors. To this end, the global constrained weighted least squares (GCWLS) optimization, as a centralized processing scheme, is presented firstly with its recursive implementation. Then, the distributed estimator of each node is constructed based on the structure of the above recursive centralized estimator and the basic idea of consensus iterative. Moreover, the sufficient conditions are established to guarantee that the iterative solution of the designed distributed estimator is able to converge to the centralized GCWLS estimates, which demonstrates that the local estimates obtained by the distributed estimator satisfy the all/global constraints. Finally, an example of collaborative navigation for formation airplanes is provided to demonstrate the effectiveness of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this article, we consider a weighted- $k$ -out-of- $n$ system having $m \geq 2$ type of components each with its own positive integer-valued weight, in which the random lifetimes of components are dependent. This system is supposed to work with performance level $k$ if and only if the total weight of functioning components of all types is at least $k$. The structure of dependence of the system component lifetimes is modeled by the copula function. Also, it is assumed that the random numbers $N_i$ of components are chosen from class $D_i$ for type $i$. The reliability of the system is obtained as a mixture of the reliability of weighted- $k$ -out-of- $n$ systems consisting of $m$ types of components with fixed number of them in terms of the probability mass function of the random vector $(N_1, \ldots,N_{m-1})$. Then, a copula-based expression for component importance in each class is obtained, and illustrative examples are presented. [ABSTRACT FROM AUTHOR]

Sum-rank metric codes have recently attracted the attention of many researchers, due to their relevance in several applications. Mathematically, the sum-rank metric is a natural generalization of both the Hamming metric and the rank metric. In this paper, we provide an Anticode Bound for the sum-rank metric, which extends the corresponding Hamming and rank-metric Anticode bounds. We classify then optimal anticodes, i.e., codes attaining the sum-rank metric Anticode Bound. We use these optimal anticodes to define generalized sum-rank weights and we study their main properties. In particular, we prove that the generalized weights of an MSRD code are determined by its parameters. As an application, in the Appendix we explain how generalized weights measure information leakage in multishot network coding. [ABSTRACT FROM AUTHOR]

In the article, the author discusses the practice of weighing suspected witches against the Bible in North America and England in the 18th and 19th centuries to determine if they are indeed witches. Also cited are the logic and origins of the custom of Bible weighing, some suspected witches like Nancy Evans in Ohio, Mary E. McDowell in Virginia, and Granny Gibbs in New York.

This study first defines the concept of weighted aggregation operators, and then mainly revolves around four very representative types of them, namely, weighted arithmetic mean, weighted geometrical mean, Sugeno Integral with weight vectors, and Shilkret Integral with weight vectors. This study proposes and defines the properties of weights pair consistency and values pair consistency with some propositions and counter examples for weighted aggregation operators. As the main finding and contribution of this study, we reveal and discuss a subtle property about weighted aggregation operators, called individual preference monotonicity. We prove or put forward counter examples to show that some of weighted aggregation operators have those proposed properties while some others do not. [ABSTRACT FROM AUTHOR]

Multiple extended target tracking (METT) is a common and challenging problem. Various solutions for METT have been proposed, however, most of them focus on the single-sensor or centralized multi-sensor scenarios. In this letter, we explore the multi-sensor METT problem in a distributed fusion framework. Specifically, there are two stages in the implementation process: 1) to perform a Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter for each sensor node, and 2) to perform a fusion by resorting to the so-called Generalized Covariance Intersection (GCI) fusion rule. In the fusion stage, we derive an approximate GGIW mixture form of the fused spatial density. Lastly, simulation experiments via a consensus sensor network are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]