Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Deep structure of the Southeast Asian curved subduction system and its dynamic process

The deep structure,material circulation,and dynamic processes in the Southeast Asia have long been an elusive scientific puzzle due to the lack of systematic scientific observations and recognized theoretical models.Based on the deep seismic tomography using long-period natural earthquake data,in this study,the deep structure and material circulation of the curved subduction system in Southeast Asia was studied,and the dynamic processes since 100 million years ago was reconstructed.It is pointed out that challenges still exist in the precise reconstruction of deep mantle structures of the study area,the influence of multi-stage subduction on deep material exchange and shallow magma activity,as well as the spatiotemporal evolution and coupling mechanism of multi-plate convergence.Future work should focus on high-resolution land-sea joint 3-D seismic tomography imaging of the curved subduction system in the Southeast Asia,combined with geochemical analysis and geodynamic modelling works.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

High-resolution crustal structure of the Yinchuan basin revealed by deep seismic reflection profiling:implications for deep processes of basin

The Yinchuan basin, located on the western margin of the Ordos block, has the characteristics of an active continental rift. A NW-striking deep seismic reflection profile across the center of Yinchuan basin precisely revealed the fine structure of the crust. The images showed that the crust in the Yinchuan basin was characterized by vertical stratifications along a detachment located at a two-way travel time（TWT） of 8.0 s.The most outstanding feature of this seismic profile was the almost flat Mohorovicˇic′ discontinuity（Moho） and a high-reflection zone in the lower crust. This sub-horizontal Moho conflicts with the general assumption of an uplifted Moho under sedimentary basins and continental rifts, and may indicate the action of different processes at depth during the evolution of sedimentary basins or rifts.We present a possible interpretation of these deep processes and the sub-horizontal Moho. The high-reflection zone, which consists of sheets of high-density, mantlederived materials, may have compensated for crustal thinning in the Yinchuan basin, leading to the formation of a sub-horizontal Moho. These high-density materials may have been emplaced by underplating with mantlesourced magma.

Deep Tectonic Processes and Superaccumulation of Metals Related to Granitoids in the Nanling MetaUogenic Province,China

The Nanling region is an important nonferrous and rare metal metallogenic province in South China, in which most of the deposits are related to granitoids in genesis. It covers southern Hunan, southern Jiangxi, Guangxi, Guangdong and Fujian provinces, with a total area of about 550,000 km2. This metallogenic province is well known in the world for its rich tungsten and tin resources. In the past 40-odd years, a vast amount of mineral exploration activities and studies of the geology of mineral deposits have been carried out and great achievements obtained in the province. This paper is focused on a discussion about the deep tectonic processes in the orogenic belt during the Mesozoic and their contribution to the superaccumulation of metals. Tectonically, this metallogenic province is composed of three units: (1) the marginal continental orogenic belt in the Southeastern Coast fold system in the Yanshanian; (2) the intercontinental orogenic belt in the collision suture belt between the Yangtze and Cathay-sian plates mainly in the Caledonian; and (3) the intracontinental orogenic belt induced by subduction of the ocean crust and delimination of the mantle lithosphere in the Yanshanian. It is suggested that superaccumulation of metals in this metallogenic province was caused by the existence of mantle rooted tectonics at the depth based on comprehensive studies of geophysical information of seismic, geothermal and magnetotelluric surveys in Nanling and its adjacent areas. The Xihuashan wolframite quartz vein deposit, the Shizhuyuan W, Sn, Mo, Bi greisen-skarn deposit and the Dachang tin-polymetallic deposit are three typical examples of the deep tectonic processes. However, this kind of deep tectonic processes only act as the 'engine' of the superaccumulation of metals, which means that they should have to correspond with the super-crust ore-controlling pattern of 'lines-rows-clusters' (L-R-C). This recog-nization is expected to play an important role in assessment of mineral resources in this province.

Process window diagram of conical cups in hydrodynamic deep drawing assisted by radial pressure

Major defects in forming of conical cups are wrinkles and rupture.Hydrodynamic deep drawing assisted by radial pressure（HDDRP） is a sheet hydroforming process for production of shell cups in one step.In this work,process window diagrams（PWDs） for Al1050-O,pure copper and DIN 1623 St14 steel are obtained for HDDRP process.The PWD is determined to provide a quick assessment of part producibility for sheet hydroforming process.Finite element method is used for this purpose considering the process parameters including pressure path,and the blank material and its thickness.Numerical results are validated by experiments.It is shown that the sheets with less initial thickness and higher strength show better formability and uniformity of thickness distribution on final product.The results demonstrate that the obtained PWD can predict appropriate forming area and probability of rupture or wrinkling occurrence under different pressure loading paths.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Characterization and evaluation of brittleness of deep bedded sandstone from the perspective of the whole life-cycle evolution process

The quantitative determination and evaluation of rock brittleness are crucial for the estimation of excavation efficiency and the improvement of hydraulic fracturing efficiency.Therefore,a“three-stage”triaxial loading and unloading stress path is designed and proposed.Subsequently,six brittleness indices are selected.In addition,the evolution characteristics of the six brittleness indices selected are characterized based on the bedding effect and the effect of confining pressure.Then,the entropy weight method(EWM)is introduced to assign weight to the six brittleness indices,and the comprehensive brittleness index Bcis defined and evaluated.Next,the new brittleness classification standard is determined,and the brittleness differences between the two stress paths are quantified.Finally,compared with the previous evaluation methods,the rationality of the proposed comprehensive brittleness index Bcis also verified.These results indicate that the proposed brittleness index Bccan reflect the brittle characteristics of deep bedded sandstone from the perspective of the whole life-cycle evolution process.Accordingly,the method proposed seems to offer reliable evaluations of the brittleness of deep bedded sandstone in deep engineering practices,although further validation is necessary.

Geochemical Characteristics of Volcanic Rocks from ODP Site 794,Yamato Basin:Implications for Deep Mantle Processes of the Japan Sea

Deep mantle processes and the dynamic mechanism of magmatism in the Japan Sea Basin are important processes that have not been studied in detail. In this paper, systematic evaluation of basalt samples from the ocean drilling program Site 794 in the Japan Sea was performed, which included petrography, whole-rock major- and trace-element analysis, Sr-Nd-Pb isotopic composition, and electron microprobe analysis of plagioclase and clinopyroxene. These basalts belong to the tholeiitic series with porphyritic texture and massive Ca-rich plagioclase, clinopyroxene, and minor olivine phenocrysts. The basalts are characterized as flat rare earth elements and high-field-strength elements with remarkably low ratios of （La/Yb）N （0.75-2.51）, significant positive anomalies of Ba, Sr, and Rb and no Eu anomaly （dEn = 0.99-1.36）. The samples showed relatively high 87Sr/86Sr （0.70425- 0.70522）, 207pb/204pb （15.511-15.610）, and 208pb/204pb （38.064-38.557） values and a low 143Nd/144Nd ratio （0.51271-0.51295）. The basalts from Site 794 can be divided into upper, middle, and lower volcanic rocks （UVR, MVR, and LVR） on the basis of their stratigraphic level. The MVR was geochemically derived from the depleted mantle, whereas the UVR and LVR originated from a nondepleted and relatively enriched mantle source with contributions from subducted Pacific plate fluid and sediments. Use of geothermobarometers indicates that the crystallization pressure for the UVR and LVR （6.25-11.19 kbar） was significantly higher than that of the MVR （3.48-5.84 kbar）. The UVR and LVR may have been derived from the low-degree （5%-10%） partial melting of spinel lherzolite, while the MVR originated from a shallower mantle source with a high degree （10%-20%） of partial melting. In addition, the geochemical characteristics of the samples are consistent with a younger age （13-17 Ma） and the depleted composition of the MVR and an older age （17-23 Ma） and slightly enriched composition of the UVR and LVR. Therefore, temporal changes in the mantle source from old and enriched to young and depleted and subsequently to old and nondepleted may have been associated with progressive lithospheric extension and thinning, as well as at least two episodes of diverse asthenospheric upwelling and pull-apart tectonic motion in the Yamato Basin.

Study on Eogene Basalts: Implication of the Deep Process over the Bohaiwan Basin Evolution

The present paper describes the characteristics of Cenozoic basalt in the Bohaiwan basin and its implication of the control of deep process over the basin evolution. The large scale Eogene basalts lying on the basement of the Bohaiwan basin belong to alkaline series and subalkaline series. The basalt magma originates at a depth of 48-76 km and a temperature of 1 300-1 400 ℃ with the mantle partial melting degree of 8%-14%. In Eogene period, the rising of the top of asthenosphere from 100-140 km to 50-70 km led to the strong extension and thinning of the overlying lithosphere, which was stretched at an average rate of 0.41 cm/a and the β value from 1.9 to 2.3. At the same time, it triggered the great scale rifting in the earth crust, forming large rift basins.

Numerical simulation study of the failure evolution process and failure mode of surrounding rock in deep soft rock roadways

Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender&Age Assessment

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills.Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images.The permutation and combination of these features realized satisfactory accuracies for a set of limited groups.In this paper,assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images.A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process.Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image.A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female.The experimentations are conducted on the datasets of Radiological Society of North America(RSNA)of about 12442 images.Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%.Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years.Process analytics encompasses identification,monitoring,and improvement of the processes through knowledge extraction from historical data.The evolution of Artificial Intelligence(AI)-enabled Electronic Health Records(EHRs)revolutionized the medical practice.Type 2 Diabetes Mellitus(T2DM)is a syndrome characterized by the lack of insulin secretion.If not diagnosed and managed at early stages,it may produce severe outcomes and at times,death too.Chronic Kidney Disease(CKD)and Coronary Heart Disease(CHD)are the most common,long-term and life-threatening diseases caused by T2DM.There-fore,it becomes inevitable to predict the risks of CKD and CHD in T2DM patients.The current research article presents automated Deep Learning(DL)-based Deep Neural Network(DNN)with Adagrad Optimization Algorithm i.e.,DNN-AGOA model to predict CKD and CHD risks in T2DM patients.The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD.This model helps in alarming both T2DM patients and clinicians in advance.At first,the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing.Besides,a Deep Neural Network(DNN)is employed for feature extraction,after which sigmoid function is used for classification.Further,Adagrad optimizer is applied to improve the performance of DNN model.For experimental validation,benchmark medical datasets were used and the results were validated under sev-eral dimensions.The proposed model achieved a maximum precision of 93.99%,recall of 94.63%,specificity of 73.34%,accuracy of 92.58%,and F-score of 94.22%.The results attained through experimentation established that the pro-posed DNN-AGOA model has good prediction capability over other methods.

Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.

Magmatism, Deep Processes and Gold Deposits in Eastern Hebei, China

The eastern Hebei Province of China is one of the major concentrating areas of gold mineralization in eastern China, which is an important part of the circum Pacific magmatic tectonic metallogenic belt. There are three types of gold deposits in terms of the characteristics of host rocks. Jinchangyu type gold deposit is situated in the Archean metamorphic basement. Yuerya type gold deposit occurs within the Yanshanian granite. Lengkou (or Wanzhuang ) type gold deposit is located within the covering strata of the Mesoproterozoic dolomitite. These 3 types of gold deposits are very similar in many respects. These deposits formed at Mesozoic and their spatial distribution is closely related to Yanshanian granite. The mineralization characteristics of these gold deposits are very similar. The characteristics of sulfur isotopic and lead isotopic compositions show that the gold deposits in this area are derived from the mantle and deep crust, and are related to Mesozoic magmatism. The gold deposits in this area are believed to be the products of crust mantle exchange and resulted from multistage gold mineralization. Finally a mineralization model of gold deposit in eastern Hebei of China is proposed.

Ideas for Accelerating Development of Deep Processing Industry of Agricultural Products--A Case Study of Nanchong City

In order to provide certain references for further deepening the development of processing industry of agricultural products,this paper analyzed and elaborated the basic principles,construction priorities and safeguard measures of the development of deep processing industry of agricultural products in Nanchong City of Sichuan Province. Besides,it made a scientific planning for accelerating the deep processing of agricultural products in Nanchong City in 2018-2020,to ensure the full implementation of fine and deep processing of agricultural products.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.