One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a ...customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.
Non-invasive techniques for the assessment of respiratory disorders have gained increased importance in recent years due to the complexity of conventional methods. In the assessment of respiratory ...disorders, machine learning may play a very essential role. Respiratory disorders lead to variation in the production of speech as both go hand in hand. Thus, speech analysis can be a useful means for the pre-diagnosis of respiratory disorders. This article aims to develop a machine learning approach to differentiate healthy speech from speech corresponding to different respiratory disorders (affected). Thus, in the present work, a set of 15 relevant and efficient features were extracted from acquired data, and classification was done using different classifiers for healthy and affected speech. To assess the performance of different classifiers, accuracy, specificity (Sp), sensitivity (Se), and area under the receiver operating characteristic curve (AUC) was used by applying both multi-fold cross-validation methods (5-fold and 10-fold) and the holdout method. Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC> 82%, and accuracy > 99%). The conclusion was that the proposed classifiers may provide an aid in the simple assessment of respiratory disorders utilising speech parameters with high efficiency. In the future, the proposed approach can be evaluated for the detection of specific respiratory disorders such as asthma, COPD, etc.
•Industry 4.0 is studied from operations planning point of view.•A chronological literature review of integrated operations planning is done.•A novel agent-based “integrated yet distributed” ...operations planning approach is developed where first time in the literature, four operations functions are integrated together.•Problem is solved for a real, complex and dynamic manufacturing environment.•First time a comprehensive performance evaluation and performance under dynamic conditions are explored to generalize the value of the proposed approach.
Present paper envisages the need for an innovative operations planning system to handle the challenges and opportunities offered by next industrial revolution called Industry 4.0 or smart manufacturing. In specific, to embrace the increasing level of automation in manufacturing industries, the obligation of joint consideration of multiple operations functions is realized. On the other hand, quick response to dynamic conditions created by machine failures, change in demand, uncertainty in supply, etc., is important in captivating the advantages of the digitization in industries. Easing out the computational complexity, imposed by the integration of multiple functions, therefore, becomes an important aspect of next generation manufacturing planning systems. Consequently, in this paper, an agent-based approach is engineered around the opportunities offered by modern digital factory viz., intelligence at the shop-floor and ubiquity of wireless communications. While intelligence at shop-floor allows distributing the decision-making tasks to various functional agents, the communication among the agents makes it feasible to incite integrated view through the coordination agent. The approach is demonstrated for a representative industrial environment of an automotive plant. Further, comparison over conventional approaches, computational comparison, effect of degree of integration, and performance of the approach under dynamic conditions are investigated. Finally, the approach is comprehensively evaluated to analyze its robustness and implications in various manufacturing settings. This extensive investigation shows that the proposed operations planning system has capability to apprehend the benefits from next generation intelligent factory.
To solve the difficulty in selecting the crossover probability and mutation probability in genetic algorithms, a fuzzy immune algorithm based on adaptive estimation of crossover probability and ...mutation probability in a fuzzy reasoning system is proposed, and it is used in the parameter optimization design of a two-degree-of-freedom PID controller. According to the experiment and simulation results, classic genetic algorithm evolution tends to halt after 37 generations, with a fitness value of 7.135, whereas fuzzy genetic algorithm evolution tends to stop after 20 generations, with a fitness value of 7.486. The 2-DOF PID controller that was created can give the system strong target value following and interference suppression features at the same time.
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•Aerial oxidation of primary product radicals are studied which obtain from cyc‐CF2CF2CF2CF = CH− + OH radical reaction.•Density functional theory (DFT) are applied using M11 ...functional along with 6–311++G(d,p) basis set.•Reaction mechanism and thermochemistry are determined for all reaction steps.•All reaction pathways are explored on potential energy surface diagram.•COF2, CHO-CFO and CHO-CF2CF2CF2CFO compounds are identified as the end degradation product.
In this manuscript, aerial oxidation of primary product radicals has been studied which are obtained from the reaction of cyc‐CF2CF2CF2CF = CH − with •OH. Geometry optimization and frequency calculations of all species were performed using M11 functional along with 6–311++G(d,p) basis set. We have explored all reaction species including transition states on the potential energy surface (PES) diagram. In addition to this, we have also determined the thermochemistry (i.e. ΔrH0 and ΔrG0) of all the successive reaction steps involved in the reaction. From PES and thermochemistry results, it has been found that COF2, CHO-CFO and CHO-CF2CF2CF2CFO are the end degradation product.
The aromatic rice represents a smaller but independent rice collection, the quality of which is considered to be highly acceptable. Farmers are interested in growing aromatic rice due to high premium ...market price. The prime objective of this study was to enhance genetic improvement of aromatic rice. Combining ability analysis (GCA and SCA) and gene action are studied in a set of 7 × 7 half-diallel crosses. Twenty-one hybrids along with their seven parents were assessed in randomized complete block design. Different quantitative characters were used to estimate the magnitude of heterosis. GCA and SCA significance for all traits revealed the importance of both additive and nonadditive genetic components. Several genes determine quantitative traits, with each gene having very little impacts and being easily influenced by environmental factors. Pusa Basmati-1 and Govindobhog were the best combiners among the seven parents. In terms of per se performance, heterosis, and SCA effects on seed yield per plant and important yield qualities, the crosses BM-24 Deharadun Pahari, Baskota × Tulaipanji, and Pusa Basmati-1 × Tulaipanji may be of interest. Because of its interconnected processing properties, ANN can play a critical role in this experiment. As a result, the current study was carried out to collect data and validate it using an artificial neural network (ANN) on the combining ability, gene action, and heterosis involved in the expression of diverse fragrant rice features. Using ANN, the validation of the result was done and it was found that the overall efficiency was approximately 99%.
Agricultural mechanization information in our country has the main problems existing in the management and utilization. The analysis of China’s agricultural mechanization management model and related ...software is presented based on combining modern science and technology as well as the development of agricultural mechanization management information system based on network software to standardize the management information collection, processing, storage and transmission, agricultural mechanization management information science, standardization, automation, etc. According to the analysis, the output target speed after fusion is more stable, and the stability is increased by 59.59% compared with the single-point GNSS velocity measurement data, and by 18.32% compared with the data measured by the binocular vision velocity measurement system. It has realized the goal of accurate speed measurement from low speed to high speed. In particular, it has solved the problems such as vehicles unable to complete positioning and vehicle skidding caused by trees blocking GNSS satellite signals during field operations.
Background: Selection of adequate size double lumen tube (DLT) is complicated by marked inter-individual variability in morphology and dimensions of tracheobronchial tree. Computerized tomography ...(CT)-guided left bronchus width measurement has been used to predict adequate size DLT in European and Singapore population; however, no such data exist for Indian population who are racially different. We compared the effect of DLT size selection based on CT-guided bronchial width measurement to the conventional method of DLT selection on the adequacy of both lungs isolation and on the safety margin of right-sided DLT. Methods: Fifty-five adults scheduled to undergo thoracotomy were enrolled in this prospective observational study. An appropriate size left- or right-sided DLT with outer diameter 0.5-1 mm smaller than the CT-measured bronchial width was selected for the isolation of lungs. Adequacy of separation was checked using fiberoptic bronchoscope. The safety margin of selected right-sided DLT size was calculated from CT-measured right upper lobe bronchus width and diameter of right upper lobe ventilation slot of the DLT. Results: Adequate separation of lungs was achieved in 92.7% of studied population, 90.9% in males, and 95.4% in females. Among these, 54.9% patients required different sized DLT as compared to conventional method. Overall safety of margin of right-sided DLTs was comparable between two methods of DLT selection (median IQR 4.8 (3.5-6.8) vs. 6.59 (3.5-7.8), P = 0.317). DLT size with adequate isolation of lung correlated with height, tracheal width (TW) on chest X-ray, and age of the patients. A formula to calculate DLT size based on these variable was derived. Conclusion: CT-measured bronchial width predicts the appropriate DLT size better than conventional method. In the absence of CT scan facility, patient height, age, and chest X-ray TW may be used to predict DLT size with reasonable accuracy.
Stroke is one of the lethal diseases that has significant negative impact on an individual's life. To diagnose stroke, MRI images play an important role. A large number of images are being produced ...day by day such as MRI (Medical Resonance Imaging), CT (Computed Tomography) X-Ray images and many more. Machine Learning algorithms are less efficient and time-consuming in localization of such medical images. Object detection using deep learning can reduce the efforts and time required in screening and evaluation of these images. In the proposed paper, several approaches such as RCNN (Region-based Convolutional Neural-Network), Fast R-CNN (Fast Region-based Convolutional Neural Network), Faster R-CNN (Faster Region-based Convolutional Neural Network with Region proposal Network), YOLO (You Only Look Once), SSD (Single-Shot Multibox Detector) and Efficient-Det are listed which can be used for stroke localization and classification. Comparison of RCNN, Fast R-CNN, Faster R-CNN, YOLO, SSD and Efficient-Det with accuracy are also present in this paper. A Chart of the Data Set available for object detection is also considered in this paper. By The maP (Mean-Average Precision) and the accuracy of every single method, it is identified that the speed and accuracy need to poise.
After surgical excision of myxoma recurrence usually happens adjacent to the initial origin site. We report a case of recurrent myxomas in a young male patient that had biatrial recurrence with one ...tumor originating very unusually from the base of the anterior mitral leaflet. Intraoperative transesophageal echocardiography was instrumental in localizing the site of the origin of left atrial myxoma from the base of the anterior mitral leaflet and in detecting an additional myxoma attached to the wall of the right atrium.
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