Optical Memory and Neural Networks最新文献

Abstract

In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 U, and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.

Abstract

Agronomic pests cause agriculture to incur financial losses because they diminish production, which lowers revenue. Pest control, essential to lowering these losses, involves identifying and eliminating this risk. Since it enables management to take place, identification is the fundamental component of control. Utilizing the pest’s traits, visual identification is done. These characteristics differ between animals and are intrinsic. Since identification is so difficult, specialists in the field handle most of the work, which concentrates the information. Researchers have developed various techniques for predicting crop diseases using images of infected leaves. While progress has been made in identifying plant diseases using different models and methods, new advancements and discussions still offer room for improvement. Technology can significantly improve global crop production, and large datasets can be used to train models and approaches that uncover new and improved methods for detecting plant diseases and addressing low-yield issues. The effectiveness of machine learning and deep learning for identifying and categorizing pests has been confirmed by prior research. This paper thoroughly examines and critically evaluates the many strategies and methodologies used to classify and detect pests or insects using deep learning. The paper examines the benefits and drawbacks of various methodologies and considers potential problems with insect detection via image processing. The paper concludes by providing an analysis and outlook on the future direction of pest detection and classification using deep learning on plants like peanuts.

In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 U, and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.

We consider the problem of obtaining a measurable assessment of the quality of empirical training data selected by experts. This problem can be solved in those cases where the data can be displayed in the form of histograms. This class includes any diagrams of frequency of occurrence of linguistic objects in samples, for example, lemmas in a text. It also includes discretized temporal signals from different branches of science, technology, and medicine. The proposed method, as well as other known methods, is based on the use of weight functions. With its help, the weight of each histogram is defined as the sum over all its columns of the products of column height by the value of weight function for the corresponding column. However, in contrast to the well-known approaches, the weight function in the proposed approach is not found empirically, but on the basis of the following variation principle. The weight function is considered optimal if the weight of the lightest histogram found with its help is greater than or equal to the weight of the lightest histogram determined by any other weight function. The application of the developed approach to the task of thematic classification of ad texts on electronic trading floors showed that for the selected topics approximately 90% of the lemmas (words) encountered in the training corpus had the weight equal to zero, and almost all words with nonzero weight were semantically related to the topic.