A Deep Learning Approach to Industrial Corrosion Detection

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection methods.While recent studies have made progress,a common challenge is the low accuracy of existing detection models.These models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource use.The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and EfficientNetB0.By leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial settings.This advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the field.The results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these challenges.Both CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable.The photovoltaic(PV)mechanism produces sunbeams’green energy without noise or pollution.The PV mechanism seems simple,seldom malfunctioning,and easy to install.PV energy productivity significantly contributes to smart grids through many small PV mechanisms.Precise solar radiation(SR)prediction could substantially reduce the impact and cost relating to the advancement of solar energy.In recent times,several SR predictive mechanism was formulated,namely artificial neural network(ANN),autoregressive moving average,and support vector machine(SVM).Therefore,this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction(OMBGRU-SRP)for energy management.The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process.To accomplish this,the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data.Next,the MBGRU model is derived using BGRU with an attention mechanism and skip connections.At last,the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization(SBO)algorithm to attain maximum prediction with minimum error values.The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild.Many experiments were conducted to demonstrate the enhanced SR prediction performance.The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.

MA-Res U-Net:Design of Soybean Navigation System with Improved U-Net Model

Traditional machine vision algorithms have difficulty handling the interference of light and shadow changes,broken rows,and weeds in the complex growth circumstances of soybean fields,which leads to erroneous navigation route segmentation.There are additional shortcomings in the feature extractFion capabilities of the conventional U-Net network.Our suggestion is to utilize an improved U-Net-based method to tackle these difficulties.First,we use ResNet’s powerful feature extraction capabilities to replace the original U-Net encoder.To enhance the concentration on characteristics unique to soybeans,we integrate a multi-scale high-performance attention mechanism.Furthermore,to do multi-scale feature extraction and capture a wider variety of contextual information,we employ atrous spatial pyramid pooling.The segmented image generated by our upgraded U-Net model is then analyzed using the CenterNet method to extract key spots.The RANSAC algorithm then uses these important spots to delineate the soybean seedling belt line.Finally,the navigation line is determined using the angle tangency theory.The experimental findings illustrate the superiority of our method.Our improved model significantly outperforms the original U-Net regarding mean Pixel Accuracy(mPA)and mean Intersection over Union(mIOU)indices,showing a more accurate segmentation of soybean routes.Furthermore,our soybean route navigation system’s outstanding accuracy is demonstrated by the deviation angle,which is only 3°between the actual deviation and the navigation line.This technology makes a substantial contribution to the sustainable growth of agriculture and shows potential for real-world applications.

基于可持续性理念的竹编文化家具设计研究

目的对竹编形态进行研究并设计一款竹编文创家具产品。方法在可持续设计的框架下,通过对竹编家具设计需求进行归纳汇总,运用FAHP方法识别关键设计点。继而使用模糊层次分析法FAHP运算得出亟需解决的视觉需求,应用人工智能算法中深度学习方法,构建竹编纹样数据集,通过迭代收敛后提炼出特殊的纹理设计方案。对结构和环境方面的需求通过建立质量屋HOQ模型,将用户需求转化为设计要求,解决纹样视觉和产品设计间的矛盾,应用工程领域的39项参数与TRIZ的40项发明原理,对矛盾进行创造性解决,优化设计与生产过程。结论该设计方案展示了可持续性材料、定量分析方法和高效生产之间的协同效应,验证了人工智能算法在竹编纹样构建中的可持续,竹材料来源的可持续,生产方式的可持续性等优势。

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'resources.Sustainable Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations.Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this work.We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph store.First,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is described.Then,a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework.The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare organizations.Our approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval times.It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

影响第二语言习得深入持久性学习的生物价值系统

普遍认为第二语言学习在"关键期"前比在"关键期"后更有效,但是无论是少年儿童还是成人学习者所获得的语言习得水平相差很大。第二语言习得成功的可变性不一定是受关键期的影响,而是受情绪/情感的驱动,情绪基于人的生物价值系统:自我平衡价值系统和社会平衡价值系统,这两种价值系统是建立细胞体价值的基础。生物价值系统形成情感记忆,通过情感过滤评价环境刺激,指导着人们的学习,特别是像第二语言那样深入持久性的学习。

高中美术学科深度学习中的持续性评价研究

持续性评价是倡导深度学习过程中提出的一种评价方式。文章主要论述高中美术学科深度学习采用持续性评价时,应以核心素养和学业质量标准为依据,应与学习目标、学习内容保持一致性,应采用多样化的评价方式贯穿于整个单元学习之中。持续性评价不仅要针对学生对美术知识与技能的掌握程度,更要针对学生解决问题时体现出的美术学科核心素养发展水平。

人工智能背景下的课程与教学范式转变

当前人工智能技术已经渗透到了教育领域的诸多方面,并引发了学生学习、课程与教学的系统变革。在人工智能背景下,深度学习、个性化学习、自适应学习、人机协同学习逐渐成为主流的学习方式,学习方式的变革直接推动了课程与教学范式的转变。就课程而言,课程的技术性范式越来越突出,藉由人工智能等技术的帮助,在课程取向上,更加注重不同学科之间的交叉及融合;在课程形态上,开始由静态、纸质、单一性到动态、在线和网络化的转变;在课程内容上,逐渐从一种固定、统一、有限转向了私人定制、无限和开放;就教学而言,在教学目标上,更加注重学生软技能(Soft Skills)和核心素养的培养;在教学方式上,开始聚焦于新技术、新媒介的融合和应用;在教学内容上,转向了对人工智能等新兴领域的关注和教授;在教学评价上,更加注重评价的过程性、精准性、数据化和个性化,等等。基于人工智能的时代背景考量,未来的课程与教学宜重点把握以下几点方向:重点关注可持续发展教育;注重对学生创造力、沟通、合作以及批判性思考等核心素养的培养;着力提升学生的“人工智能素养”教育;加强全人教育以及课程与教学的人文主义关怀。

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trialand-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly.Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Recent advancements of the intelligent transportation system(ITS)provide an effective way of improving the overall efficiency of the energy management strategy(EMSs)for autonomous vehicles(AVs).The use of AVs possesses many advantages such as congestion control,accident prevention,and etc.However,energy management and traffic flow prediction(TFP)still remains a challenging problem in AVs.The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs.In this view,this paper presents novel sustainable energy management with traffic flow prediction strategy(SEM-TPS)for AVs.The SEM-TPS technique applies type II fuzzy logic system(T2FLS)energy management scheme to accomplish the desired engine torque based on distinct parameters.In addition,the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer(BMO).For accurate TFP,the bidirectional gated recurrent neural network(Bi-GRNN)model is used in AVs.A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics.The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

计算机人工智能在作物病害识别与防治中的应用

本研究系统综述了当前计算机人工智能技术在作物病害领域的应用研究,主要涵盖了深度学习、图像处理和机器学习等方面。通过分析传统方法在作物病害识别中的局限性,强调了引入计算机人工智能技术的迫切性。同时,本研究详细介绍了计算机人工智能技术在作物病害检测和防治中的手段,如卷积神经网络(CNN),并对当前研究中存在的不足之处进行了深入剖析,包括算法泛化能力、数据质量和社会接受度等方面。通过对当前研究的全面分析,我们认为计算机人工智能技术在作物病害防治中有着非常广阔的应用前景,进一步的研究应更加注重跨学科研究、技术实际应用、数据隐私保护以及可持续发展等方面。

How artificial intelligence uses to achieve the agriculture sustainability:Systematic review

The generation of food production that meets the rising demand for food and ecosystem security is a bigchallenge. With the development of Artificial Intelligence (AI) models, there is a growing need to use them toachieve sustainable agriculture. The continuous enhancement of AI in agriculture, researchers have proposedmany models in agriculture functions such as prediction,weed control, resource management, advance care ofcrops, and so on. This article evaluates on a systematic review of AI models in agriculture functions. It also reviewshow AI models are used in identified sustainable objectives. Through this extensive review, this paper discussesconsiderations and limitations for building the next generation of sustainable agriculture using AI.

Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

This paper develops a trustworthy deep learning model that considers electricity demand(G)and local climate conditions.The model utilises Multi-Head Self-Attention Transformer(TNET)to capture critical information from𝐻,to attain reliable predictions with local climate(rainfall,radiation,humidity,evaporation,and maximum and minimum temperatures)data from Energex substations in Queensland,Australia.The TNET model is then evaluated with deep learning models(Long-Short Term Memory LSTM,Bidirectional LSTM BILSTM,Gated Recurrent Unit GRU,Convolutional Neural Networks CNN,and Deep Neural Network DNN)based on robust model assessment metrics.The Kernel Density Estimation method is used to generate the prediction interval(PI)of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations.The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems.

基于深度学习的有机合成复习课教学实践——常见高分子材料的用途与合成

基于深度学习理念,以“常见高分子材料的用途与合成”为学习主题,开展关于有机合成复习课的教学实践。指出在有机合成的复习课,开展深度学习有利于激发学生的学习需求、调动学生高阶思维、让学生迁移运用所学知识。基于深度学习的教学设计需要把握挑战性学习主题、深度学习目标、深度学习活动和持续性评价4大要素。提出课前应引导学生开展挑战性学习主题的项目调研;课上应创设引导学生深度学习的问题情境,促进学生高阶思维的发展;课后应让学生以思维导图的形式理顺有机合成的一般方法等,为教师开展基于深度学习的教学实践提供参考。