Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

Prototype for Integrating Internet of Things and Emergency Service in an IP Multimedia Subsystem for Wireless Body Area Networks

In recent years, the application of the Internet of Things （IoT） has become an emerging business. The most important concept of next-generation network for providing a common global IT platform is combining seamless networks and networked things, objects or sensors. Also, wireless body area networks （WBANs） are becoming mature with the widespread usage of the IoT. In order to support WBAN, the platform, scenario and emergency service are necessary due to the sensors in WBAN being related to wearer＆#39;s life. The sensors on the body detect a lot of information about bioinformatics and medical signals, such as heartbeat and blood. Thus, the integration of IoT and network communication in daily life is important. However, there is not only a lack of common fabric for integrating IoT with current Internet and but also no emergency call process in the current network communication envi-ronment. To overcome such situations, the prototype of integrating IoT and emergency call process is discussed. A simulated boot-strap platform to provide the discussion of open challenges and solutions for deploying IoT in Internet and the emergency commu-nication system are analyzed by using a service of 3GPP IP multimedia subsystem. Finally, the prototype for supporting WBAN with emergence service is also addressed and the performance results are useful to service providers and network operators that they can estimate their migration to IoT by referring to this experience and experiment results. Furthermore, the queuing model used to achieve the performance of emergency service in IMS and the delay time of the proposed model is analyzed.

Application of Hidden Markov Models in Speech Command Recognition

In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature values.Subsequently,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command recognition.The recorded speech length was three Chinese characters,which were used to test the method.Five phrases pronounced mixing various human voices were recorded and used to test the models.The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

Qualitative Analysis of a Fractional Pandemic Spread Model of the Novel Coronavirus (COVID-19)

In this study,we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal(natural host)to humans.We establish a model of fractional-order differential equations to discuss the spread of the infection from the natural host to the intermediate one,and from the intermediate one to the human host.At the same time,we focus on the potential spillover of bat-borne coronaviruses.We consider the local stability of the co-existing critical point of the model by using the Routh–Hurwitz Criteria.Moreover,we analyze the existence and uniqueness of the constructed initial value problem.We focus on the control parameters to decrease the outbreak from pandemic form to the epidemic by using both strong and weak Allee Effect at time t.Furthermore,the discretization process shows that the system undergoes Neimark–Sacker Bifurcation under specific conditions.Finally,we conduct a series of numerical simulations to enhance the theoretical findings.

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.

Lateral Migration and Nonuniform Rotation of Square Particle Suspended in Poiseuille Flow

A square particle suspended in a Poiseuille flow is investigated by using the lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration of Segré-Silberberg effect is observed for the square particle, accompanied by the nonuniform rotation and regular wave. To compare with the circular particle, its circumscribed and inscribed squares are used in the simulations. Because the circumscribed square takes up a greater difference between the upper and lower flow rates, it reaches the equilibrium position earlier than the inscribed one. The trajectories of the latter are much closer to those of circle;this indicates that the circle and its inscribed square have a similar hydrodynamic radius in a Poiseuille flow. The equilibrium positions of the square particles change with Reynolds number and show a shape of saddle, whereas those of the circular particles are virtually not affected by Reynolds number. The regular wave and nonuniform rotation are owing to the interactions of the square shape and the parabolic velocity distribution of Poiseuille flow, and high Reynolds number makes the square rotating faster and decrease its oscillating amplitude. A series of contours illustrate the dynamic flow fields when the square particle has successive postures in a half rotating period. This study is beneficial to understand the motion of anisotropic particles and the dendrite growth in dynamic environment.

Low-Cost Posture Recognition of Moving Hands by Profile-Mold Construction in Cluttered Background and Occlusion

In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.

The Study and Application of the IoT in Pet Systems

The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has been a growing demand. Thus, how to raise pets in an easy way has been the main issue recently. This study examines the ability of computation, communication, and control technologies to improve human interaction with pets by the technology of the Internet of Things. This work addresses the improvement through the pet application of the ability of location-awareness, and to help the pet owners raise their pet on the activity and eating control easily. Extensive experiment results demonstrate that our proposed system performs significantly help on the kidney disease and reduce the symptoms. Our study not only presents the key improvement of the pet monitor system involved in the ideas of the Internet of Things, but also meets the demands of pet owners, who are out for works without any trouble.

The Design of a Cloud and Mobile Healthcare System

The aging society will become a serious problem for most countries in the world. Under the constraint of limited medical resource, the self-health management becomes important. In this paper, a mobile healthcare system is implemented. One can easily monitor his/her physiological data through the using of a smartphone that is wirelessly connected to different medical detection devices. A cloud database is established for storing and analysing these physiological data. The guidance of suitable physical exercises to individuals is then given in the system. This paper shows the details of the system implementation.

Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput.This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems.First,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data searching.Next,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job execution.As a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively.

Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.However,the entire code transformation process has encountered a time-consuming problem.Therefore,the objective of this study is to speed up the code transformation process signi􀀀cantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash(VSH)algorithm and replacing LCS with a piecewise longest common subsequence(PLCS)algorithm to faster the veri􀀀cation process in the test phase.Besides the code transformation model GPT-2,this study has also introduced MicrosoMASS and Facebook BART for a comparative analysis of their performance.Meanwhile,the explainable AI technique using local interpretable model-agnostic explanations(LIME)can also interpret the decision-making ofAImodels.The experimental results show that VSH can reduce the number of quali􀀀ed programs by 22.11%,and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result,the proposed approaches can signi􀀀cantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.

Design of Semipersistent Resource Allocation in LTE-V Network

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced(LTE-A),which was developed by 3rd Generation Partnership Project(3GPP).Device-to-device(D2D)communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B(eNB).Moreover,D2D transmission can support vehicles as a vehicle-to-vehicle(V2V)environment.It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety(and efficiency).However,radio resources are limited in radio networks.A vehicle transmits through D2D in Long Term Evolution-Vehicle(LTE-V)mode-3 standard,and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously.When using the same radio resources,the probability of interference may increase.This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network.Moreover,resource allocation under different cycles was analyzed,and a scheme for resource selection is proposed based on cycle size.The proposed Semi Persistent Gain Aware Resource Allocation(SP-GARA)scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance.

Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

Enhancement of GAO's Multiparty Quantum Secret Sharing

A multiparty quantum secret sharing (MQSS) protocol with two-photon three-dimensional Bell states was proposed by Gao [Commun. Theor. Phys. 52 (2009) 421] recently. This study points out that the performance of Gao's protocol can be much improved by using the technique of decoy single photons and carefully modifying the protocol to remove some unnecessary unitary operations, devices, and transmissions.