Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Colorectal cancer screening:The value of early detection and modern challenges

The screening of colorectal cancer(CRC)is pivotal for both the prevention and treatment of this disease,significantly improving early-stage tumor detection rates.This advancement not only boosts survival rates and quality of life for patients but also reduces the costs associated with treatment.However,the adoption of CRC screening methods faces numerous challenges,including the technical limitations of both noninvasive and invasive methods in terms of sensitivity and specificity.Moreover,socioeconomic factors such as regional disparities,economic conditions,and varying levels of awareness affect screening uptake.The coronavirus disease 2019 pandemic further intensified these challenges,leading to reduced screening participation and increased waiting periods.Additionally,the growing prevalence of early-onset CRC necessitates innovative screening approaches.In response,research into new methodologies,including artificial intelligence-based systems,aims to improve the precision and accessibility of screening.Proactive measures by governments and health organizations to enhance CRC screening efforts are underway,including increased advocacy,improved service delivery,and international cooperation.The role of technological innovation and global health collaboration in advancing CRC screening is undeniable.Technologies such as artificial intelligence and gene sequencing are set to revolutionize CRC screening,making a significant impact on the fight against this disease.Given the rise in early-onset CRC,it is crucial for screening strategies to continually evolve,ensuring their effectiveness and applicability.

Application of transient Rayleigh wave in detection of tunnel lining void area

Transient Rayleigh wave detection is a high-precision nondestructive detection method.At present,it has been widely used in shallow exploration,but rarely used in tunnel lining quality detection.Through the tunnel lining physical model experiment,the layout defects of the double-layer reinforcement lining area were detected and the Rayleigh wave velocity profile and dispersion curve were analyzed after data process-ing,which finally verified the feasibility and accuracy of Rayleigh wave method in detecting the tunnel lining void area.The results show that the method is not affected by the reinforcement inside the lining,the shallow detection is less disturbed and the accuracy is higher,and the data will fluctuate slightly with the deepening of the detection depth.At the same time,this method responds quite accurately to the thickness of the concrete,allowing for the assessment of the tunnel lining’s lack of compactness.This method has high efficiency,good reliability,and simple data processing,and is suitable for nondestructive detection of internal defects of tun-nel lining structure.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

Quality Detection of Fresh Longan Fruit in Hainan

[Objective] This study was conducted to investigate the fresh eating-quality of Hainan Iongan. [Methed] The quality analysis was conducted on on-season and off-season Iongan, which are main cultivars in Hainan consistent in maturity. In order to understand the quality characteristics of Hainan fresh Iongan, saccharide contents, vitamin C content, edible rate, TSS, weight of single fruit, weight per fruit cluster and pesticide residues were detected in this study. [Result] There were no significant differences in quality between on-season and off-season Iongan, and the content of TSS in off-season fruit was slightly higher than that in on-season Iongan. The two main cultivars ‘Shixia＇ and ＇Chuliang＇ in Hainan differed significantly in edible quality. The weight of single fruit and edible rate of ‘Chuliang＇ were slightly higher than those of ‘Shixia＇ Iongan, and their sucrose contents were nearly equivalent. Vitamin C and TSS contents in ‘Shixia＇ Ionganwere higher than those in ‘Chuliang＇.‘Shixia＇ had a monosaccharide content significantly higher than ‘Chuliang＇, while its sucrose content was lower than ‘Chuliang＇. Only low contents of residual cypermethrin and diflubenzuron were detected in pericarp, and the contents of the 9 pesticides in fruit flesh were all lower than their detection limits. [Conclusion] Longan fruit produced in Hainan could all be eaten safely.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Quality Detection of Hainan Honey Pomelo

Quality analysis was performed to major cultivars, ‘Hongxin pomelo’ and ‘Wuzi pomelo＇ with uniform maturity. Total soluble solid （TSS）, titratable acid, vita- min C, juice yield, edible rate, single-fruit weight and pesticide residues of the 2 cultivars were detected. The results showed that： the TSS contents of ‘Hongxin pomelo＇ and ‘Wuzi pomelo＇ were in the range of 8%-12%, the vitamin C contents were in the range of 0.3-0.6 g/L, the juice yields were in the range of 56%-68%, the edible rates were in the range of 52%-68%, and the single-fruit weights were in the range of 1-2.5 kg. The titratable acid content was higher in ‘Hongxin pomelo＇ than in ‘Wuzi pomelo＇. Only Dursban and imidacloprid were detected at low con- centrations in pericarp, and the residues of the 3 pesticides were all lower than their detection limits. Therefore, honey pomelo produced in Hainan all could be ate safely.

Study of multi-rate multi-user detection based on supervision decision

Multi-user detection (MUD) based on multirate transmission in code division multiple access (CDMA) system is discussed. Under the requirement of signal interference ratio (SIR) detection at base station and framework with parallel interference cancellation, a supervision decision algorithm based on pre-decision of probabilistic data association (PDA) and hard decision is proposed. The detection performance is analyzed and simulation is implemented to show that the supervision decision algorithm improves the detection performance effectively.

Quality Analysis of Herbal Medicine Products Prepared from Herba Sarcandrae by Capillary Electrophoresis with Electrochemical Detection

A capillary electrophoresis with electrochemical detection（CE-ED） method was developed for the quality analysis of herbal medicine products prepared from the same herb of Herba Sarcandrae： Fufang Caoshanhu tablets, Qingrexiaoyanning capsules, and Xuekang oral liquids. Under the optimal analysis conditions, the low detection limit[l.0×10^-7 mol/L（S/N=3）] and the wide linear range（1.0×10^-7-1.0×10^-4 mol/L） were obtained for quality standard compound of isofraxidin. The precisions of the peak current and the migration time（as RSDs） for the real sample analysis were 2.0%-2.6%, and 1.2%-1.8% for isofraxidin, respectively. The contents of isofraxidin detected were 15.77 μg/tablet, 0.48 mg/capsule, 1.2 mg/ampoule（Jiangxi）, and 0.44 mg/ampoule（Dalian） for Fufang Caoshanhu tablets, Qingrexiaoyanning capsules, and Xuekang oral liquids from different manufacturers, respectively. Quality estimate was conducted by comparing the contents of isofraxidin in the herbal medicine products with the demanded values of Chinese pharmacopeia. In addition, based on their own unique CE-ED profiles（namely, CE-ED electropherograms） the Xuekang oral liquids from the different manufacturers could be easily identified.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and banks.However,monitor-ing the video continually at a quicker pace is a challenging job.As a consequence,security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful actions.The anomalous action does not occur at a high-er rate than usual occurrences.To detect the object in a video,first we analyze the images pixel by pixel.In digital image processing,segmentation is the process of segregating the individual image parts into pixels.The performance of segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.In this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

Optimal control of a quality supervision profit model for the electronic intermediary

Online customers execute transactions without inspecting products could expect to encounter risks of receiving products with unsatisfactory qualities,especially in food trans-actions.Thus quality supervision plays a key role in the establishment of trust as well as in the management of risk between online customers and sellers.Most papers in this field are in qualitative nature.In this paper,a quality supervision profit(QSP)model is formulated as a discrete-time optimal control problem.It is a quantitative approach,and it broadens the scope.of current research in the area.The quality efort level of online sellers(QELa)and the quality supervision level of the electronic intermediary(QSLm)are considered together with their cor-responding profit in the proposed model.The aim is to optimize an overall profit.A case study arising from Suichang's food traceability system(FTS)of farm produce online transaction is carried out in details.The results reveal that QELs,QSLm and the profit distribution coeffi-cient have a strong influence upon the profits of both sides.Finally,some concluding remarks,including potential further research topics,are given.

National Quality Supervision & Inspection Center for Refractories

National Quality Supervision ＆ Inspection Center for Refractories （NRQS） was set up in 1986 and was authorized in 1990 by State Bureau of Quality and Technical Supervision （SBQTS） as the state supervision ＆ inspection center （Certificate SBQTS No. 101）. In 1993, NRQS was granted Import ＆ Export Authorized Laboratory for national refractories by the State Administration for Entry Exit Inspection and Quarantine （Certificate No. 5302）. In 1995, NRQS was granted the national testing organization by State Committee of Science ＆ Technology and SBQTS. In September 2000,

Examining Construction Quality Supervision and Organization Procedures for BOT Projects by Comparing with DBB Projects

Adopting the BOT （build-operate-transfer） delivery method has brought opportunities and challenges in construction time and quality for urban infrastructure. Many studies investigate the upstream issues of BOT projects such as concessionaire selection criteria and risk factors. But the project execution becomes complicated such as the escalated quality supervision organization and procedure at the construction stage. This extra supervision may have constituted hidden redundancies as waste to economic sustainability. This paper investigated construction quality supervision of BOT projects to pinpoint adequate degree of supervision and detect redundant organization and procedures. The study adopted ten subway construction projects as cases in which five were from the traditional design-bid-build （DBB） and five from the BOT methods. Thirty six project participants including owners, contractors, designers, and concessionaire managers of the ten projects were interviewed to tell their views on these issues. The quality supervision measures of the DBB projects were used as the baseline and compared with those of the BOT projects. The research results show that the construction quality seems no difference for the DBB and BOT projects. Construction quality comes out of supervision but excessive supervision organization and procedures do not necessarily improve quality. This finding indicates that adequate degree of supervision is enough to achieve required quality to save immense paperwork for large construction projects.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

The Research to Quality Supervision and Management of Power Enterprises

Intensive in-depth development of resources, centralized purchasing and gradually expand under the category of environment, this paper studies how to use information technology tools to construction quality supervision and management system that focuses on the quality of supervision and project management, quality control process management, quality monitoring statistics report management, platform management-related content in four areas. Through the construction quality supervision and management system, to better enhance the quality of electric power enterprise management level, to ensure that the material quality, quantity of time and supplies. strengthening quality supervision and management can not only effectively reduce material procurement costs, but also enhance the competitiveness of enterprises.

AQSIQ Popularizing Nation-wide Launching of Electronic Network for Product Quality Supervision

According to AQSIQ, it has made an effort to popularize the launching ofelectronic ''network for product quality supervision in the whole nation. China Product QualitySupervision Electronic Network is a nation-wide network, which employs modern informationtechnology, network technology and encoding technology to supervise enterprises'' products in anelectronic way. The new system labels digital information on all items of products that are includedin the network.

Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to secure.Moreover,the rapid growth of IoT devices in homes increases the risk of cyber-attacks.Intrusion detection systems(IDS)are commonly employed to prevent cyberattacks.These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures.Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been unsuccessful.In this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks.Specifically,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset.To accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network models.We also propose two baseline models to demonstrate the performance of the proposed models.Generally,both models achieve high accuracy in detecting the majority of these nine attacks.