Weight Information Research Articles

LPG CYLINDER AUTO FILLING AND GAS LEAKAGE DETECTION

The project "LPG Cylinder Auto Filling and Gas Leakage Detection" integrates IoT technology for automating the LPG cylinder filling process and detecting gas leakages. The system utilizes a Load Cell connected to a Node MCU to measure the weight of the cylinder. Concurrently, a gas sensor interfaced with an Arduino board detects any gas leakages. Through serial communication between the Node MCU and Arduino, the weight data from the Load Cell is transmitted to the Arduino, which then displays both the weight information and gas leakage status on an LCD display. Additionally, a Python installed PC is employed to facilitate email notifications. Upon detecting a gas leak or a decrease in weight, the Arduino sends this information to the Python PC via serial communication. Subsequently, email alerts are generated and sent to the designated recipients, notifying them of the detected gas leakage and the need for refilling the cylinder. Moreover, in case of a gas leak, the system activates a pump to initiate the refilling process. Through this integrated approach, the project aims to enhance safety and efficiency in LPG cylinder management. Keywords : Node MCU, Arduino UNO, Gas sensor, Load cell, Python installed PC, Buzzer, Relay, DC Motor.

Using a novel vector length stability index (VLSI) to evaluate soil aggregate stability: A preliminary study

Soil aggregate stability is an important soil characteristic which can represent the improvement of soil structure after revegetation. Due to the high heterogeneity of soil, large-scale sampling is usually necessary to estimate the spatial variation of aggregate stability across landscapes. Although there are many methods and corresponding indices for analysis of aggregate stability, many of them are not suitable for investigations with a large number of samples due to time-consuming experimental practices. To avoid unnecessary costs of time and labor in the long-term land degradation and restoration monitoring across large regional scales, simple indices of aggregate stability would help monitoring across large regional scales. For this reason, we introduced a new, simple, and normalized index, the vector length stability index (VLSI) and its weighted form (wVLSI) to describe soil aggregate stability. The VLSI shares key information of aggregate size distribution with mean weight diameter (MWD), but it is naturally normalized and dimensionless. This new index was tentatively tested to describe soil aggregate stability of the 0–30 cm soil layers under artificial plantations, natural grassland, abandoned farmland, and farmland in production in a small watershed located in the Loess Plateau of China. The results showed that the aggregate stability of the 0–20 cm layers in abandoned farmland could be recovered near to the condition under artificial plantations just after 2-year abandonment, but the aggregate stability hardly changed in the 20–30 cm layer. The aggregate stability of the 0–10 cm layer in natural recovered grassland is close to that in top soil layers under artificial plantations, but the stability in the 10–30 layers appeared as low as that in farmland. Because of its simple form and low data requirements, the VLSI could be an alternative efficient tool for soil aggregate stability evaluation when a large number of samples are needed.

Environmental Sustainability of Building Materials in Turkey: Reference Information Recommendations for European Green Deal Declarations

This study provides weight and normalization reference information for declaring the environmental information of building materials produced and exported in Turkey. Reference information was first determined for the global warming potential based on greenhouse gas (GHG) emissions, which is the subject of the European Union Green Deal Carbon Border Adjustment Mechanism (CBAM). For a more holistic approach, reference information is also recommended for environmental impact categories acidification, air pollution, ecological toxicity, eutrophication, fossil fuel depletion, human health, indoor air quality, land use, ozone depletion, photochemical smog formation, and water depletion, in addition to GHG emissions. Reference information is determined based on the life cycle assessment (LCA) methodology defined in the international standards ISO 14040 and ISO 14044. Semi-structured interviews were held with twenty-one industry stakeholders in Turkey to determine the weight reference values. The results obtained from the semi-structured interviews were combined using the analytic hierarchy process (AHP) method. Normalization reference information was determined by compiling Turkey’s national emission values. The suggested reference information has been tested using a case study. Total environmental impact scores were calculated for floor coverings and exterior wall finishes, including global warming potentials based on GHG emissions, and eleven other environmental impact categories. The findings support the need to use regional reference information in Turkey. The reference information recommended in this study can be used both in declarations within the scope of the EU Green Deal and in other possible environmental impact declarations resulting from building materials.

Entropy Induced Pruning Framework for Convolutional Neural Networks

Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification tasks. However, the majority of existing methods are sensitive with respect to the model parameters, and their pruning results may be unsatisfactory when the original model is trained poorly. That is, they need the original model to be fully trained, to obtain useful weight information. This is time-consuming, and makes the effectiveness of the pruning results dependent on the degree of model optimization. To address the above issue, we propose a novel metric named Average Filter Information Entropy (AFIE). It decomposes the weight matrix of each layer into a low-rank space, and quantifies the filter importance based on the distribution of the normalized eigenvalues. Intuitively, the eigenvalues capture the covariance among filters, and therefore could be a good guide for pruning. Since the distribution of eigenvalues is robust to the updating of parameters, AFIE can yield a stable evaluation for the importance of each filter no matter whether the original model is trained fully. We implement our AFIE-based pruning method for three popular CNN models of AlexNet, VGG-16, and ResNet-50, and test them on three widely-used image datasets MNIST, CIFAR-10, and ImageNet, respectively. The experimental results are encouraging. We surprisingly observe that for our methods, even when the original model is trained with only one epoch, the AFIE score of each filter keeps identical to the results when the model is fully-trained. This fully indicates the effectiveness of the proposed pruning method.

Normative values of motor performance and their relationship with BMI status in Hong Kong preschoolers

This study aimed to establish sex- and age-specific reference values for motor performance (MP) in Hong Kong preschoolers aged 3–5 years old and examine the relationship between MP and BMI status. A cross-sectional study was conducted among 5579 preschoolers in Hong Kong. Three MP tests were administered, and height and weight information were collected. GAMLSS was used to compute the normative values of the motor tests. Boys outperformed girls in activities requiring muscle strength and power, while girls outperformed boys in activities requiring balance and coordination. The MP scores increased with age for both overarm beanbag throw and standing long jump for both sexes, while the one-leg balance scores showed larger differences between P50 and P95 in older preschoolers. Children with excessive weight performed worse in standing long jump and one-leg balance compared to their healthy weight peers. This study provides valuable information on the MP of preschoolers in Hong Kong, including sex- and age-specific reference values and the association between BMI status and MP scores. These findings can serve as a reference for future studies and clinical practice and highlight the importance of promoting motor skill development in preschoolers, particularly those who are overweight or obese.

Plug-and-play multi-dimensional attention module for accurate Human Activity Recognition

Unlike image data, Human Activity Recognition (HAR) data is collected through sensors placed on different parts of the body. Each sensor may have varying importance for different human actions. Attention mechanisms can dynamically identify the significance of signals from different sensors. However, existing attention modules often operate along channel or spatial dimensions, producing one-dimensional or two-dimensional weights, and considering neurons within every channel or spatial location as equivalent. This might constrain their capacity to acquire more distinctive cues. In this paper, we present a novel Three-Dimensional Weight Attention Module (WAM) that considers both spatial and weight information of each channel. Specifically, we commence by introducing an energy function and subsequently assess the significance of each neuron through optimization. Unlike conventional channel or spatial attention modules, this module can compute 3D attention weights for feature maps without additional parameters. Experiments conducted on four diverse HAR datasets showcase that the Three-Dimensional Weight Attention Module can seamlessly integrate with convolutional models, significantly enhancing model performance without introducing extra computational burden. To further highlight the utility of WAM, we measure the actual inference time on an embedded platform.

WGDPool: A broad scope extraction for weighted graph data

Graph pooling is a commonly used operation in graph neural networks to reduce the size of graph representation. To extract key information, pooling and representation need to be coupled. We propose a graph pooling method for weighted graphs called WGDPool (Weighted Graph Dual Pooling). Unlike traditional graph representation learning methods, the weight information of edges is also fed into convolutional graph neural networks (ConvGNN) to obtain graph representations. Dual branch convolutional graph neural networks is designed to learn the nodes’ and edges’ embeddings independently, and they are fused into a comprehensive representation of graph data. Pooling, as a tool of feature extraction and scale reduction of graph representation, adopts a differentiable version of k-means clustering and a multi-item parameterized loss function. Cut loss, orthogonality loss, clustering loss, and reconstruction loss are simultaneously considered. By parameterization, WGDPool is competent for diverse graph tasks. WGDPool outperformed other graph pooling methods in such common supervised and unsupervised tasks as biological or chemical classification, bibliography clustering and integrated circuit partition, demonstrating the effectiveness of our proposed pooling method.

Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic sets

With the development of the internet economy, e-commerce has rapidly risen, and a large number of small and micro e-commerce enterprises have emerged. However, these enterprises have low financial information transparency, small scale, and high development uncertainty. Therefore, combining the characteristics of the internet economy, it is of great significance to dynamically evaluate credit risk. This not only helps to enhance the quality and rationality of credit risk evaluation results, but also helps to improve financing efficiency and reduce financing risks. The credit evaluation for small and micro enterprises is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and TOPSIS method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as an effective tool for characterizing uncertain information during the credit evaluation for small and micro enterprises. In this paper, the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is inaugurated to solve the MAGDM under 2TLNSs. Finally, a numerical case study for credit evaluation for small and micro enterprises is inaugurated to confirm the proposed method. The prime contribution of this paper are outlined: (1) The information entropy based on score function and accuracy function are built on the 2TLNSs to obtain weight information; (2) an integrated the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is established to cope with MAGDM; (3) An illustrative example for credit evaluation for small and micro enterprises has accomplished to illustrate the 2TLNN-TODIM-TOPSIS; (4) some comparative analysis are employed to verify the 2TLNN-TODIM-TOPSIS method.

MABAC-based evaluation of multimedia technology application effect in film and television post-production

Computer multimedia technology first appeared in the late 1980s, which is a functional upgrade based on the development of digital technology. It can achieve functions such as information collection, processing, and storage. Multimedia technology has received widespread attention in the entire computer field and has become a popular technology. Against the backdrop of continuous technological development, various electronic products are presented to people, bringing convenience to their lives. At the same time, they also play a huge role in the work of some industries. The application of multimedia technology, advanced editing software, and digital equipment in the production of video works can present good results and showcase excellent film and television works for people. The application effect evaluation of multimedia technology in the film and television post-production is frequently viewed as a multi-attribute group decision-making (MAGDM) issue. The maximizing deviation method was used to obtain the weight information under intuitionistic fuzzy sets (IFSs). Depending on the MABAC (multi-attributive border approximation area comparison) method and IFSs, this paper design the intuitionistic fuzzy MABAC (IF-MABAC) method to assess the application effect evaluation of multimedia technology in the film and television post-production. Eventually, an example about application effect evaluation of multimedia technology in the film and television post-production and some comparative analysis have been employed to verify the superiority of the designed IF-MABAC method.

A consensus-based single valued neutrosophic model for selection of educational vendors under metaverse with extended reality

Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the comfort and well-being of learners.This paper proposes a decision-making model to address the complex task of selecting educational vendors within the Metaverse with a focus on extended reality (XR). Recognizing the challenges inherent in such selections, this work presents a consensus-based measurement of alternatives and ranking according to compromise solution (MARCOS) model, incorporating single-valued neutrosophic (SVN) information and SVN Dombi-Archimedean weighted aggregation operators. Attribute weights crucial for decision-making are determined through an optimization tool employing cross-entropy measures with partial weight information. The evaluation encompasses nine criteria, including cost, security, privacy, pedagogical alignment, configuration, maintenance, level of control, output tools, degree of immersion, availability of XR learning contents, and time. Four leading vendors including Victory XR, ARuVR, zSpace, and Varjo Technologies are assessed, with zSpace emerging as the most favorable alternative with the highest overall utility value of 0.6889. The paper further conducts sensitivity analysis and performance comparisons to establish the robustness of the ranking results, providing a comprehensive methodology for educational stakeholders addressing the evolving dynamics of the Metaverse.

Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets

As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple attribute decision making (MADM) issue. Aczel-Alsina t-norm and t-conorm operations have the true advantage of changeability by adjusting a decision parameter. power averaging (PA) operator allowed decision attributes to strengthen each other through weight information during fusion, thereby eliminating the influence of subjective weights on the decision results. Motivate through classical Aczel-Alsina operators, PA operator and the single-valued neutrosophic sets (SVNSs), in this paper, the single-valued neutrosophic number Aczel-Alsina power averaging (SVNNAAPA) operator and the single-valued neutrosophic number Aczel-Alsina power weighted averaging (SVNNAAPWA) operator are constructed under SVNSs. The SVNNAAPWA operator is built for MADM. Eventually, an example about English blended teaching quality evaluation and some selected comparative decision analysis is used to depict the SVNNAAPWA method.

Enhanced computer network security assessment through employing an integrated LogTODIM-TOPSIS technique under interval neutrosophic sets

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people’s daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique.

Generalized correlation coefficients of intuitionistic multiplicative sets and their applications to pattern recognition and clustering analysis

ABSTRACT Intuitionistic multiplicative preference relations (IMPRs) and intuitionistic multiplicative sets (IMSs) play a significant role in real-life problems that contain unsymmetrical and nonuniform information. Correlation coefficients are critical tools for evaluating such information, especially in medical areas and clustering analysis, where the relationship between objects in the given data is required. Despite the importance of this subject, there is only one approach in the literature regarding the correlation coefficients of IMSs and existing coefficients have certain disadvantages. In this paper, we propose a parametric generalisation of these correlation coefficients on IMSs and apply them to medical diagnosis, taxonomy, and clustering. To that end, some disadvantages of existing correlation coefficients are listed first. Then, with some theoretical work, we derive a parametric generalisation of these coefficients and their weighted forms. To better illustrate how the parametric generalisation of correlation coefficients improves the results, numerical parametric solutions of existing examples are presented with detailed comparisons. Moreover, a novel algorithm is introduced for clustering using proposed correlation coefficients in IMSs. Finally, three real-life examples are provided to demonstrate the superiority of the proposed generalised correlation coefficients and the clustering algorithm in specific applications.

Assessment of factors causing landslides using the Analytical Hierarchy Process (AHP) method

Landslides are Indonesia's second most common disaster in the last ten years. Landslides cause losses, reaching hundreds of billions and threatening human safety. For this reason, it is necessary to take action to reduce the negative impact of landslides. Physical and non-physical mitigation can occur before, during, and after a disaster. The most essential thing in disaster mitigation is knowing the major factors that cause landslides. This research aims to analyze the major and minor factors that cause landslides using the Analytical Hierarchy Process (AHP) and the Likert scale. Respondents in this research are experts in disasters, especially landslides—respondents from universities in Indonesia, practitioners, and people involved in disaster mitigation. Validity and reliability tests were carried out on each question used in the questionnaire. The research results show that the landslide disaster was caused by a disruption in balance that occurred due to internal and external disturbance factors. The major external factors that cause slope failures are the cutting of hills and changes in land use to built-up areas. In contrast, the major internal factors that trigger landslides are heavy rainfall and steep topographic conditions. The results of this research in the form of weight and scoring values for each factor causing landslides can be used as a reference for mapping landslide areas to identify areas with the potential for landslides.

Weight Monitoring System for Truck Load

Abstract: For cargo to be moved safely and effectively, a truck load weight monitoring system is required Overloading of trucks not only poses a risk to road safety but also causes excessive wear and tear on the vehicle, resulting in higher maintenance costs andreduced fuel efficiency. To address these challenges, an advanced weight monitoring system has been developed to measure and monitor the weight of truck loads using HX711 loadcell. When it comes to monitoring and managing the weight of truck loads. Overloading of trucks has been a longstanding issue in the industry. It not only compromises road safety but also has detrimental effects on the vehicles themselves. Excessive weight can lead to increased braking distances, compromised steering control, and increased risk of tire blowouts, all of which contribute to higher accident rates. This system utilizes advanced technologies and sensors to accurately measure and monitor the weight distribution of cargo carried by trucks. By providing real-time weight information using GPS and GSM modules, operators and fleet managers can make informed decisions to ensure compliance with weight regulations and prevent overloading

Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion.

In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions.

Projection-based TOPSIS method to MAGDM in dual probabilistic linguistic information environment

The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods.

A novel uncertain information modeling method based on cosine similarity and cross entropy under spherical uncertain linguistic fuzzy set

Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers’ (DMs’) cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs’ and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis.

A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.

The functional role of spatial anisotropies in ensemble perception.

The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.