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In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan's characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings. [ABSTRACT FROM AUTHOR]

Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain. [ABSTRACT FROM AUTHOR]

Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more and more popular and full-grown, diverse technologies for measurement and collection of business data continually improve as well. Effective data analysis of and applications can be helpful to stores to make smart and quick decisions in a jiffy, so that the percentage of customer satisfaction and in-store shopping can increase to raise the total revenue. Some researchers have suggested that the number of customers who enter a store refers to a Poisson process. Based on previous research, an attribute service performance index was proposed in this paper. This paper reviewed the fuzzy one-tailed testing model of the attribute service performance index and put forward a fuzzy two-tailed testing model of two indices based on the confidence interval to verify whether the improvement had a significant effect. Now that this fuzzy evaluation model is built on the confidence interval of the index, we can diminish the chance of misjudgment caused by sampling error. Its design can incorporate the past data or expert experience. Thus, the evaluation accuracy can be retained in the case of small-sized samples. [ABSTRACT FROM AUTHOR]

As the Internet-of-Things matures, technologies for the measurement and collection of production data are also improving. What is needed next is an effective information analysis model to aid in the timely adjustment of manufacturing parameters to optimize production. Production data analysis models must be continuously and properly utilized to monitor and maintain process quality. Improving product quality helps to lengthen service life, decrease scrap and rework, and reduce the social losses caused by malfunctions and maintenance. The Taguchi capability index Cpm can fully reflect the losses and yield of processes and is a convenient and effective tool to evaluate and analyze process data in the industry. As it contains unknown parameters, we derived the upper confidence limit (UCL) of Cpm based on collected production data. Due to the fuzzy uncertainties that are common in measurement data, we then used the UCL of the index to construct a fuzzy membership function and propose a fuzzy testing decision-making model to determine whether processes are in need of improvement. Before the proposed fuzzy test methods became full-fledged, we used the concept of sample size and the rules of statistical testing to explain the motivation underlying those methods. In fact, the sample size influences the risk of misjudgment in UCL, and in practice, sample sizes are rarely large due to cost and time considerations, thereby they produce larger UCL with a corresponding decrease in accuracy and increase in risk of misjudgment. The fuzzy test methods proposed in this study are based on statistical inference, and judgement is aided by expertise, thus are capable of solving the problems associated with larger UCL. Therefore, the theoretical foundation of this fuzzy testing decision-making model is the UCL, it can lower the chance of misjudgment caused by sampling errors and increase evaluation accuracy. [ABSTRACT FROM AUTHOR]

India has shown rapid economic development and this has forced the country to adopt smart traffic management and surveillance process. The intelligent traffic management and surveillance is the basic need for development of smart cities in India. The Intelligent traffic surveillance includes the detection of moving vehicles, estimation of their speed and extracting its registration number. This paper proposes an efficient and novel approach for the detection of moving vehicles as well as estimation of their speeds for detection of any speed limit violation by using a single camera in daylight or properly illuminated environment. The proposed approach detects and tracks the vehicle passing through the surveillance area and maintains the record of vehicles position. The Region of Interest (ROI) of the approach is the dark shaded area formed under the vehicles road clearance area instead of the vehicles. The average detection accuracy achieved by proposed approach is 93.32%. This is on an average 7.54% higher than other existing approaches. The proposed approach uses cropping operation to minimize the scope of any false positive detection on both sides of road. The average tracking accuracy of the proposed approach is about 92.2%. The tracking accuracy achieved by the proposed approach is on an average 2.2% higher than the other leading approaches. The average speed estimation error in proposed approach is +2.2 km/h which is lower than other existing leading approaches. The proposed approach is also time and space efficient over the existing approaches. [ABSTRACT FROM AUTHOR]

Brain tumor is one of the deadliest cancerous diseases and their severity has turned them into the leading cause of cancer-related mortality. Automatic detection and classification of severity-level for a brain tumor using MRI is a complex process in multilevel classification and needs an improved learning method without computational complexity. In this research article, we propose an innovative Multi-Dimensional Cascades Neural Network work (MDCNet) that takes full advantage of two networks with different dimensions, which can balance the complete semantic information and high-resolution detail information of a large-volume MRI image. In stage 1, a shallow-layer-enhanced 3D location net obtains the location and rough segmentation of brain lesions. In stage 2, a high-resolution attention map is used to obtain the 2D high-resolution image slice sets from the original image and the output of stage 1. The high-resolution images pick up the lost detailed information, refining the boundaries further. Moreover, a multi-view 2.5D net composed of three 2D refinement sub-networks is applied to deeply explore the morphological characteristics of all brain lesions from different perspectives, which compensates for the mistakes and missing spatial information of a single view, increasing the stability of the whole algorithm. The robustness of the proposed model is analyzed using several performance metrics of three different data sets. Through the prominent performance, the proposed model can outperform other existing models attaining an average accuracy of 99.13%. Here, the individual accuracy for Dataset 1, Dataset 2, and Dataset 3 is 99.67%, 98.16%, and 99.76% respectively. [ABSTRACT FROM AUTHOR]

The cogent area, Probabilistic rough sets, offers methods that are used to trisect the data into positive, negative and boundary regions for optimum (α, β) pairs. These basic methods generate three regions based on a single quality, including cost, entropy, impurity, correlation and variance, thereby the best (α, β) pair is generated. The optimization of multiple qualities has significance in real-life applications; however, experiments rarely discussed the optimization of different criteria together in probabilistic rough sets. This probe conducts multi-objective optimization of uncertainty, impurity and correlation, to determine a trisection at optimal (α, β) pairs. For that, this work proposes a hybrid method that involves Weighted Sum and Artificial Bee Colony Algorithm to optimize the thresholds. The results are compared with the Information-theoretic rough sets and Game-theoretic rough sets. The proposed method outperforms regarding optimal qualities, multiple optimum thresholds, minimal size of boundary regions, and better evaluation results. By attesting the study on experimental data sets, optimal (α, β) pairs are obtained at which the uncertainty and impurity are minima. Moreover, the correlation at this threshold is reasonable. From the application viewpoint, it reduces the cost of further analysis by generating the minimum delayed decision and maximizes the benefit with optimal decisions by considering multiple optimized qualities simultaneously. [ABSTRACT FROM AUTHOR]

Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering. [ABSTRACT FROM AUTHOR]

Logistic regression as a modelling technique of rare binary dependent variables with much fewer events (ones) than non-events (zeros) tends to underestimate their probability of occurrence. The vast literature devoted to the prediction of rare binary data identifies several ways to improve predictive performance by making modifications to the likelihood estimation. We propose two weighting mechanisms for incorporation in a pseudo-likelihood estimation that improve the predictive capacity of rare binary responses in data collected in complex surveys. We multiply sampling weights by specific correctors that lead to lower root mean square errors for event observations in almost all deciles. A case study is discussed where this method is implemented to predict the probability of suffering a workplace accident in a logistic regression model that is estimated with data from a survey conducted in Ecuador. [ABSTRACT FROM AUTHOR]

Due to the increasing demand on aquaculture, the continuous monitoring of water quality and characteristics are significant for maximizing the yields. Even though many physicochemical parameters are available for monitoring the water quality, the knowledge of domain experts are expected to analyze these parameters to find the final decision about the quality of water. In order to utilize the knowledge of the domain experts for water quality artificially, we have developed a functional tangent decision tree algorithm which, predict the water quality of the ponds based on the physiochemical parameters. The proposed method of predicting the water quality consists of three important steps such as, uncertainty handling, feature selection using reduct and core analysis, classification using the functional tangent decision tree. The proposed functional tangent decision tree is constructed by utilizing a new function called functional tangent entropy for the selection of attributes and split points. Then, the proposed work is experimented with the real database and compared with the existing classifiers to prove its better performance. The proposed work obtained the maximum classification accuracy of 95% as compared with the existing work. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal operation management of the distribution feeder reconfiguration (DFR) considering the uncertainty effects. In contrast to the conventional objective functions, this paper considers the System Average Interruption Frequency Index (SAIFI) as a reliability index. Meanwhile, the total active power losses and the voltage deviation objective functions are considered as the other targets too. In order to make the analysis more practical, the uncertainty associated with the active and reactive load forecast errors are modeled in a stochastic framework based on 2 m Point Estimate Method (PEM). In the proposed stochastic optimization framework, an external memory called repository is defined to store the non-dominated solutions which are found during the optimization process. Also, a fuzzy based clustering technique is defined to keep the size of the repository within the predefined limits. Since the proposed problem is a nonlinear, discrete complex optimization problem, this paper proposes an intelligent self adaptive modified optimization algorithm based on θ-firefly algorithm to solve the optimal multi-objective stochastic DFR problem suitably. The proposed self-adaptive modification method consists of three sub-modification techniques which let each firefly choose the sub-modification method that best suits its situation adaptively. The feasibility and superiority of the proposed method is tested on a standard IEEE test system. [ABSTRACT FROM AUTHOR]