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A paper-based colorimeter for absorbance and concentration measurement of the food colouring dye is proposed. The paper-based colorimeter system consists of a white LED as light source, paper-based cuvette holder, and smartphone light sensor. The paper-based colorimeter with smartphone light sensor is low-cost, mobile and real-time for the detection of colouring dye concentration. The detection response of the paper-based colorimeter system was found to be linear with the colouring dye concentration in the range from 0 to 0.025 g/mL with a correlation coefficient (R2) 0.89±0.04. The experimental results show that this paper-based colorimeter system is highly sensitive and have a potential application, from student labs to small industries.

The traditional Chinese folk paper-cutting art has a long history. It is a traditional folk art form created by working people to satisfy their spiritual and material pursuits. It originates from life and returns to life. With the pace of the times, folk paper-cutting has undergone an innovative transformation and development through the interweaving, penetration and integration of contemporary design concepts. Through the combination of network big data, it provides a large number of references for modern graphic design decoration and function combination, graphic context inheritance, color language, modeling language, etc.

FMEA is a widely selected tool for the application of machinery breakdown prevention. This research adopted FMEA technique to reduce and prevent the breakdown of the important equipment of a large commercial paper machine that has capacity of 200,000 tonnes per annum located in the Northeast area of Thailand. This research accomplished the improvement of preventive maintenance in other machines for rapid implementation and improved machine inspections. After implementation, the downtime of paper machine belonging to maintenance department responsibility reduced significantly 5 hours per month by average, machine availability, OEE, and MTBF increased 0.46%, 0.51%, and 10 hours respectively. The machine downtime has been on the downward trend.

Overall Equipment Effectiveness (OEE) is a method of measuring the usage effectiveness of the equipment. This method is known as an application of a Total Productive Maintenance (TPM) program. The ability to clearly identify the source of problem and it causal factors is the main advantage of this method since the improvement effort becomes focused. Pulp and Paper Industries is one of the manufacturing companies that produce dissolving pulp products (rayon fibre). Based on data from the fibre line department, for the period of January - July 2016, the production process of rayon fibre often experienced constraints due to the high downtime and losses in the pulp machine. This is caused of using production process equipment that had not operate optimally. It is important to find out the source of the problem and its causal factors before the company makes any improvement effort. This study aims to identify equipment losses and measure the achievement of OEE values in the pulp machine. The measurement result shows that the average of the effectiveness of pulp machine for the period of January - July 2016 was 74.01% and based on Japan Institute of Plant Maintenance (JIPM) the value has not reached the standard that is > 85%, however, there is still possibility for improvement. One of the loss factors that give the most significant effect on the overall equipment effectiveness of the pulp machine is the reducing speed, where it comprises 27.6% of the loss. One way to minimize the loss is by maintaining the actual speed of operation and maintaining wear on each pulp machine roll.

Paper artifacts are contaminated by external factors in the process of preservation such as foxing. For the problem of backward technology of rapid detection of foxing on paper artifacts, a method based on hyperspectral imaging technology is proposed to detect foxing spots on paper artifacts. After selecting the region of interest and obtaining the corresponding average reflectance, the difference in the average reflectance is found after comparing the healthy regions with the diseased regions. Using band operation and minimum noise fraction to observe the characteristics of foxing image, although there is overlap in different parts, the distribution distinction between moldy and healthy regions is obvious; K-nearest neighbor method and BP neural network are applied to establish the spectral discrimination model of paper artifacts with foxing spots, and the overall discrimination rate of the two methods is 73.3% and 85%, respectively. The results show that hyperspectral imaging can be used for the identification of foxing spots, but the distinction between different parts is not good, and the discrimination effect still needs to be improved.

In Today’s world technologies such as Internet of Things, Cloud Computing as well as Fog Computing are growing at an exponential rate which depend upon each other directly or indirectly. The Internet of Things can be described as a network of substantial matter such as cars, washing machines, refrigerator which can interact with each other through internet. Billions of devices will be IoT enabled in near future and generate enormous amount of data but IoT devices has some limitations like storage capabilities, processing capabilities and utilization of resources which can only be handled by integrating it with cloud technology. Cloud model provide environment in which software, Infrastructure, sharable pool of configurable resources, virtual environment, sensors, hardware and database is provided as a utility for IoT devices and users. In cloud computing paradigm some limitations exist for example distance of the data source from multi-hop, geological unified structure, latency, heterogeneity and many more. To address such limitations, Fog computing approach can be used to bring computing assets nearer to IoT devices. Fog computing is an enhancement of the cloud-based Network and computing services. It provides computational and storage services of cloud proximate to IoT devices. This paper provides an overview regarding the cloud computing uses in IoT devices and issues or problems that occur during integration. Handling of problems that occurs during integration of cloud with IoT can be done through fog computing. The purpose of this survey is to understand the concept of fog computing to improve the existing system of Integration of Cloud with IoT.

Wireless technology these days is very quickly becoming crescent. A network that was recently wired to get online was required. Just wired phones have been a thing of the past. In the past four decades, mobile networks have thrived immensely. The starting point was the 1G cellular concept where ‘G’ stances for generation linkages. This had fully developed very rapidly, generating 1G, 2G, 3G and gradually transferring into 4G from generation to generation. And now people are utilizing the 4G networks. 5G network will nearly stretch its wings to conquer this complex world of cell technology. Integrated 5G work is continuing, with complete service planned in 2020. The development of 5G technology is the perfect solution for the many problems facing us today with today’s innovations. 5G will become an intelligent technology that will limit to a single global uniform body the number of different innovations. This paper is mostly about the evolution of mobile wireless networks like 1G and 5G as well as how they vary and their benefits and drawbacks.

The results of CFD research using the VOF method with Open Foam open source are presented. In the process of study, we built a digital model allowing us to analyze and predict the process of interaction of the dampening solution with substrate (paper) surface in the measuring cell of the PDA ultrasonic device. Discretization of the area for calculation was created using the blockMesh utility. A block-structural hexahedral computational grid has been constructed. The computational grid area is presented in such a way that the number of cells increases in the direction of the paper sample. The visualization of calculations in the ParaView package coincides with the time intervals obtained on the PDA ultrasonic measuring device. The practical significance of the study lies in the realization of the possibility to evaluate the dampening solution parameters, to control the modes of its supply, taking into account the use of modern papers and printing systems.

Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

This article analyzes the common classifications of computer test paper algorithms. This article includes random method, backtracking method, genetic algorithm, random location search method, ant colony algorithm, gene expression algorithm, etc. The author obtains the expected calculated values by studying the technologies of setting control parameters, creating initial populations, calculating individual fitness, optimizing preservation strategies, performing genetic operations, and generating new populations. The author’s purpose is to improve the application effect of the computer test paper algorithm and the practicability of the information in the question bank.

Breast Cancer is most popular and growing disease in the world. Breast Cancer is mostly found in the women. Early detection is a way to control the breast cancer. There are many cases that are handled by the early detection and decrease the death rate. Many research works have been done on the breast cancer. The Most common technique that is used in research is machine learning. There are many previous researches that conducted through the machine learning. Machine learning algorithms like decision tree, KNN, SVM, naïve bays etc. gives the better performance in their own field. But now days, a new developed technique is used to classify the breast cancer. The new developed technique is deep learning. Deep learning is used to overcome the drawbacks of machine learning. A deep learning technique that is mostly used in data science is Convolution neural network, Recurrent neural network, deep belief network etc. deep learning algorithms gives the better results as compared to machine learning. It extracts the best features of the images. In our research, CNN is used to classify the images. Basically our research is based on the images and CNN is most popular technique to classify the images. In present paper, reviews of all authors are conducted.

Examination paper analysis is important for students to improve their learning efficiency. Traditional paper examination papers are difficult to sort out, which makes collecting mistakes for future review both time-consuming and laborious, either by handwriting or existed software tools. To easy the process, we propose a layout analysis method combined with the conditional generative adversarial network (CGAN). The traditional semantic segmentation structure is improved and used as a generator in the network, while a discriminator is designed to make the segmentation results more accurate. The motivation is that the discriminator can judge the authenticity of the image, so it can help reduce the unreasonable phenomenon in the semantic segmentation results of the generator. The experimental results show that by this method the examination paper image can be welly split into various components, which provides convenience for further sorting and analysis of the examination paper images.

This system uses the STM32F103 single-chip microcomputer as the control center, and the capacitance-to-digital converter FDC2214 collects the current paper value, performs data collection and transmits it to the main control single-chip microcomputer. Methods: Calculate accurate data through Kalman filtering, and accurately calculate and display the number of measured paper sheets on this basis. The device also sets a buzzer to accurately short circuit alarm. The circuit structure of the entire system is simple, and the reliability is high. Results: The system has high precision and fast speed, and can complete the basic requirements and expansion requirements of the problem. Although it is limited by time and venue, it can also have a high reference value for the design of practical applications such as detecting the number of banknotes. Conclusions: Experimental test results show that this design can complete the basic indicators required by the topic and play part of the content. This paper focuses on the hardware design, software design and test result analysis of the system.

One of the most important topics that concern many researchers is how to make information that has a high degree of confidentiality hidden and not accessible except by the people who have the authority to view it, images are one of the most important multimedia that can be used as a cover to include the data, because the image contains how much A huge amount of data that can include confidential information in it, as well as the image is one of the most multimedia that is sent and received thus that the process of messaging with pictures does not arouse suspicion, this research provided a detailed study of a group of articles that used the image as a cover in the inclusion of information Confidentiality, and focus was on the most important domains of strength in each article. Articles were studied from 2013 to 2020 that were published at the containers of Scopes, and Google Scholar.

Due to the rise of diverse finite element software, the needs to understand each of the software interface for optimization of the software usage is increasing among new users. Many researchers had roughly done their software explanation through a few simple methods such as clarifying the software user interface and framework to enhance user benefit with their software. They also provide conclusion for the explanation with a proper justification of an analysis or through interviewing user experiences. The three steps had been proven to be effective in term of giving recognition of software to new users with different background field. This paper will review the standard definition of a graphical user interface that many existing finite element software had completed and how it was exposed to the user. Also, how the software framework function for user to obtain a reliable data out of it and finally the validation of the enlighten framework and GUI for users ease in each model analysis.

Poor adherence to prescription medications causes approximately 125 000 deaths per year while an epidemic of a prescription drug overdose has been on the rise, resulting in an alarming number of deaths, 47 055 in 2014 only in the USA. Efforts from governments to address this issue are technologically inadequate. Once a patient leaves the pharmacy with a prescription drug container, no one, besides the patient, has control over the safe use of drugs. Here, we show a Do-It-Yourself (DIY) integration strategy to monitor drug intake, using a low-cost paper sensor-based flexible electronic system integrated inside the cap of a drug container. This electronic system consists of flexible modular decal sensors connected to a central flexible decal electronic interface, using a DIY compatible integration technique using an anisotropic conductive tape. We find that our DIY integration strategy gives the flexibility to develop customized versions of the system, depending on the requirements for different applications, ranging from ambient sensors to monitor the environment inside the container to security sensors that can detect any kind of mishandling with the prescription container. The add-on system is easy to install or remove, enabling an easy implementation for practical use that will ultimately mitigate drug overuse.

International audience

This paper presents the design of sustainable paper bicycle which describes the stage by stage in the production of paper bicycle. The objective of this project is to design a sustainable paper bicycles to be used for children under five years old. The design analysis emphasizes in screening method to ensure the design fulfil the safety purposes. The evaluation concept is presented in designing a sustainable paper bicycle to determine highest rating. Project methodology is proposed for developing a sustainable paper bicycle. Design analysis of pedal, front and rear wheel, seat, and handle were presented using AutoCAD software. The design optimization was performed to fulfil the safety factors by modifying the material size and dimension. Based on the design analysis results, it is found that the optimization results met the factor safety. As a result, a sustainable paper bicycle was proposed for children under five years old.

A rotating mechanical components in machineries like bearings, gears, pulleys, belt drives etc. are major components in any rotating machinery. The failure of these components leads to downtime of machines and reduction in production. Significant economic losses will be caused due to an unexpected failure of these components. Belt drives are widely employed in various industrial equipment. Finding the early fault symptoms in the belt drive is very important. This can be achieved by various methods. For detecting faults and monitoring the condition of a belt drive, the vibration signal can be used as one of the parameter. Thus, vibration signal can be used as a procedure for predictive maintenance and it is used for machinery maintenance decisions. The changes in vibration signals due to fault can be detected by employing signal processing methods. It can be used to evaluate the health status of the machinery. The nature and severity of the problem can be determined by analysing the vibration signal and hence the failure can be predicted. Signature of the fault in the machine is carried by the vibration signal. It is possible to have early fault detection by analysing these vibration signals. Different signal processing techniques are used for processing these signals. The various techniques used for fault diagnosis based on vibration analysis method are discussed in this paper. The application of the artificial intelligence techniques such as Artificial Neural Network (ANN), fuzzy sets and other emerging technologies are discussed.

One of some ways to summarize scientific papers is by employing rhetorical structure of sentences. Determining rhetorical sentence itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing semantic similarity words. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by using selected features, added previous label, and Word2Vec to capture semantic similarity words. Then, this paper shows the result of employing resampling for balancing the existing instances per class and combining resampling and Word2Vec representation itself. Every experiment is tested in two classifiers, namely IBk and J48 tree. It shows that the use of previous label, Word2Vec (Skip-Gram), and resampling improves performance. After doing all the experiments in the 10-fold cross-validation, the highest performance of F-measure is achieved 84.97% by combining Word2Vec (Skip-Gram), all features, and resampling.

Living organisms provide a vast resource to derive new and innovative designs. As aquatic and aerial organisms have adapted to movement through water and air over millions of years by the evolutionary process of natural selection, these organisms have been subjected to a severe cost–benefit analysis. Having survived this natural experimentation, a number of varied and effective solutions for propulsive systems has been attained. Animals swim and fly by employing combinations of paddling, flapping, jetting, and vortex control. Furthermore, animals use materials that are compliant and self-adjusting. Emulations of these systems can provide innovative, bioinspired propulsive mechanisms that perform better than conventional human-designed systems. Acceleration, speed, efficiency, maneuverability, and stealth in manufactured systems can potentially be improved by copying nature. The fusion of biology and engineering through biomimetics and bioinspiration presents a means to future technological improvement in marine and aerial systems. What appears in this issue of Bioinspiration & Biomimetics: Learning from nature are selected papers from the Mini-Symposium on Biomimetics & Bio-Inspired Propulsion held in Boulder, CO, USA, on 26 June 2006. The symposium was held during the 15th US National Congress of Theoretical and Applied Mechanics on 25–30 June 2006 and was planned with the primary objective of bringing together a multidisciplinary group of researchers involved in all different aspects of biological propulsion. Fourteen groups presented in three separate sessions in the morning and afternoon. Most participants were from US institutions, while a few groups from Canada and Europe also attended. In this special issue, you will find a series of papers that covers various aspects of biopropulsion. The breadth of these manuscripts—ranging from flow visualization/characterization, and computational/theoretical fluid dynamics, to biological aspects of swimming and flying—demonstrates the significance of and interest in biopropulsion. These topics are often covered in different journals and conferences aimed at particular disciplines. Holistic discussion of this subject, where various perspectives and tradeoffs are investigated, is rarely considered. This special issue tries to bring these disparate disciplines together to address key topics and potential future thrusts. The articles in this issue are grouped into three categories: flying, swimming, and more general theory. The first paper in the flying category by R J Bomphrey `Insects in flight: direct visualization and flow measurements' considers smoke flow visualization and digital particle image velocimetry in the investigation of kinematic parameters such as wingbeat frequency and amplitude and wing design in insects. Experimental investigation of bat flight is reported by X Tian et al in `Direct measurements of the kinematics and dynamics of bat flight' where the wake structure during the entire wing beat cycle was constructed. The manuscript by J D Eldredge `Numerical simulations of undulatory swimming at a moderate Reynolds number' employs a dynamically coupled flow simulation around a simple three-linkage system. The next two papers by G V Lauder et al and R Mittal et al present results from a coordinated experimental–computational study of pectoral fin swimming in a sunfish, where the focus of these papers is on detailed quantitative experimental measurements of flow and kinematics in freely swimming fish and high-fidelity three-dimensional numerical simulations of pectoral fin swimming that provide a comprehensive view of the flow field and force production. Changes in the three-dimensional geometry of a wing-like, aquatic propulsor and their hydrodynamic performance are investigated by F E Fish et al in `Passive cambering and flexible propulsors: cetacean flukes'. The first paper on wake estimation methods is `Measurement of propulsive power and evaluation of propulsive performance from the wake of a self-propelled vehicle' by P S Krueger. This paper considers the propulsive performance and proposes a method for measuring mechanical power and how to evaluate propulsive performance arising from it. Finally, the article by K Mohseni `A formulation for calculating the translational velocity of a vortex ring or pair' examines the calculation of the velocity of a vortex ring or pair, generated by any actuator, from the characteristics of the actuator itself, including the rate of energy, impulse, and circulation generation. Although this symposium does not provide all the answers in addressing bioinspired propulsion, it does serve as a starting point to provide direction for future research. As the need for higher levels of performance for propulsive systems increases, inspiration from animal systems can offer new avenues for the development of innovative technologies.

Since 1991, Measurement Science and Technology has awarded a Best Paper prize. The Editorial Board of this journal believes that such a prize is an opportunity to thank authors for submitting their work, and serves as an integral part of the on-going quality review of the journal. The current breadth of topical areas that are covered by MST has made it advisable to expand the recognition of excellent publications. Hence, in 2005 the Editorial Board decided to present 'Outstanding Paper Awards' in four subject categories: Fluid Mechanics; Measurement Science; Precision Measurements; and Sensors and Sensing Systems. 2005 Award Winners?Fluid Mechanics The Fluid Mechanics working group, chaired by Professor John Foss, was unanimous in its recommendation for the paper authored by J Chen and J Katz (Johns Hopkins University, USA) 'Elimination of peak-locking error in PIV analysis using the correlation mapping method', published in volume 16, issue 8, pp 1605?1618. The essence of the following citation was provided by Board Member Dr Mark Wernet: The paper of Chen and Katz describes a technique for eliminating the 'peak locking' bias error endemic to estimating the PIV correlation peak location. Particle image velocimetry (PIV) is used widely in both fundamental and applied fluid mechanics. In essence, a two-dimensional velocity map is extracted from two successive high-resolution images of light scattered by minute tracer particles. The incident light is derived from two laser beams which have been expanded into sheets. A precise time delay is imposed between the two laser light sheets. The cross-correlation of the scattered light intensity within corresponding small interrogation regions in the two images gives the displacement of the particles and hence the local velocity. Typically, in PIV processing, the correlation peak location is determined by fitting a curve through the correlation peak. This process is known to suffer from a bias error where the estimated displacements are grouped around integer pixel values. Background subtraction can help reduce the tendency for peak locking, but does not completely remove the bias. Other techniques for eliminating the bias are available, but require time-consuming, iterative processing. The paper of Chen and Katz describes a processing technique that removes this 'peak locking' bias by avoiding the curve fitting process altogether. Instead, the displacement necessary to map the first exposure into the second exposure is determined via a correlation mapping operation. Several example cases using synthetic data and actual PIV data are presented. The new approach is also compatible with the Subregion Image Distortion technique to further improve the quality of the PIV displacement estimates. 2005 Award Winners?Measurement Science The 2005 Measurement Science and Technology Outstanding Paper Award in the Measurement Science category has been awarded to M E Webber, T MacDonald, M B Pushkarsky (Pranalytica, Inc., USA), C K N Patel (UCLA, USA), Y Zhao, N Marcillac and F M Mitloehner (University of California, Davis, USA) for the article 'Agricultural ammonia sensor using diode lasers and photoacoustic spectroscopy', published in volume 16, issue 8, pp 1547?1553. In making their recommendation the Measurement Science working group chaired by Professor Richard Dewhurst gave the following endorsement: This paper provides an excellent description of a novel instrumentation system used to detect agricultural ammonia. The sensor system has been designed for use in dusty and humid conditions, and a portable prototype was field-tested on several days over an eight-week period, with encouraging results. In this paper, a short theoretical section is followed by instrumentation details, with a description of the optical system, the electronics, embedded programming and algorithm development. An erbium-doped fibre amplifier was used to enhance the photoacoustic signal by boosting the laser's output power, whilst maintaining the linewidth of the seed laser source. Careful laboratory calibration tests are then described, showing benchmark results of sub-ppm sensitivity, and excellent system linearity up to 100 ppm. The final section describes the condition under which field tests were conducted, and validates the photoacoustic method by cross reference with both a Draeger sensor and an ion chromatograph. Overall, the paper possesses several innovative ideas. It is a well-structured paper, containing details of the instrumentation and techniques used. It has a careful discussion of the data and its validation, in order to realize the precision demonstrated. It is an innovative system required for a topical need within environmental protection. 2005 Award Winners?Precision Measurement The 2005 Measurement Science and Technology Outstanding Paper Award in the Precision Measurement category has been awarded to Ichiko Misumi, Satoshi Gonda, Qiangxian Huang, Taeho Keem, Tomizo Kurosawa (National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology (NMIJ/AIST), Japan), Akihiro Fujii, Nahoko Hisata, Takeshi Yamagishi, Hirohisa Fujimoto (Olympus Corporation, Japan), Ken Enjoji (Olympus Systems Corporation, Japan), Sunao Aya and Hiroaki Sumitani (Mitsubishi Electric Corporation, Japan) for the article 'Sub-hundred nanometre pitch measurements using an AFM with differential laser interferometers for designing usable lateral scales', published in volume 16, issue 10, pp 2080?2090. In making their recommendation the Precision Measurements working group chaired by Dr Kenichi Fujii gave the following endorsement: This paper describes a new instrument developed for measuring the pitches of one-dimensional grating standards. In the field of nanotechnology and in the semiconductor industry, reliable one-dimensional grating standards have been solicited for calibrating length measurement in nanometre regions. In the instrument described here, an atomic force microscope (AFM) is combined with differential laser interferometers so that the atomic scale resolution of the AFM is accurately calibrated by optical interferometry, achieving an unprecedented uncertainty of 0.2 nm in measuring the pitches of about 50 nm. The instrument also calibrates the pitches in a wide scanning range. This paper analysed the uncertainty of the measurement in detail, helping to provide users with reliable certified reference materials for one-dimensional grating standards. 2005 Award Winners?Sensors and Sensing Systems The 2005 Measurement Science and Technology Outstanding Paper Award in the Sensors and Sensing Systems category has been awarded to Nicola Bowler and Yongqiang Huang (Iowa State University, USA) for the article 'Electrical conductivity measurement of metal plates using broadband eddy-current and four-point methods', published in volume 16, issue 11, pp 2193?2200. In making their recommendation the Sensors and Sensing Systems working group chaired by Professor Paul Regtien gave the following endorsement: The paper addresses two methods to measure the electrical conductivity of metal plates of various materials. In particular for ferromagnetic plates, traditional methods show relatively large measurement uncertainties. In this paper, the origins of these large inaccuracies are studied, and two alternative methods are presented that yield much better accuracies. Both methods?a broadband eddy-current and a four-point contact scheme?are extensively analysed theoretically as well as experimentally. The results are compared with other published data, for plates of brass, stainless steel and spring steel. An uncertainty analysis reveals the superiority of the newly introduced methods in the case of steel. The four-point method has the advantage of being easy to use. The paper is well-structured, with a good balance between theory and experiments, and a clear presentation of the measurement data and associated uncertainties, both in tables and graphs. The paper concludes with a convincing discussion and outlook for further research. The results are of great value in the field of non-destructive testing. The Outstanding Paper Awards, comprising a cash honorarium and certificate, will be presented to the authors of the winning papers at suitable venues in the near future. The Editorial Board would like to congratulate the winning authors and would like to encourage all researchers to think of Measurement Science and Technology as the home for your best submissions.

In 2010, the LHC experiment produced 7 TeV and heavy-ions collisions continually, generating a huge amount of data, which was analyzed and reported throughout several performed studies. Since then, physicists are bringing out papers and conference notes announcing results and achievements. During 2010, 37 papers and 102 conference notes were published and until September 2011 there are already 131 papers and 189 conference notes in preparation. This paper presents the ATLAS Analysis Papers and ATLAS Analysis Conference Notes systems, developed to monitor the entire publication procedure up to the final submission and to promote the communication among the collaboration members. The software supports the paper elaboration process, tracking the analysis results status and improvement of the paper initial version, presenting a step-by-step procedure overview and promoting communication among collaborators. Along with the increasing flow of papers and conference notes, one of the issues is the way to guarantee that all members who participate in the analysis studies are aware of not only the discussion deadlines but also of the publication process, which involves 17 steps, split in 3 different phases for papers and 10 steps in 1 phase for conference notes. By sending notifications based on predefined rules the systems inform members to approve each step and provide further information such as the approval conditions and the documents in which the publication is based on. Through the software it is also possible to manage dates and members of the editorial team. The data processing is performed by using the Glance System, the main data retrieval platform used for ATLAS information management.

We are very pleased and honoured to present this special issue of Bioinspiration & Biomimetics that gathers a set of nine peer-reviewed articles whose results were in large part presented at the International Workshop on Bio-Inspired Robots which was held in Nantes, France, from 6?8 April 2011. The participants in this event have enthusiastic memories of the workshop, which was organized within the framework of EU-funded Future Emerging Technologies (ICT/FET) projects named Angels and Lampetra and hosted by the Ecole des Mines de Nantes and the IRCCyN Lab, Nantes, France. Following the workshop in Nantes, the general topic of this special issue is therefore on bio-inspired robots. The core theme of the special issue reflects a growing interest in developing bio-inspired autonomous robots that can interact with an unknown environment. This innovative scientific approach is first based on biological observations of animals from the point of view of locomotion, perception and their sensory-motor integration. Aquatic, aerial or terrestrial locomotion modes are of paramount importance for their high capabilities in terms of maneuverability/agility and energy efficiency. In the present special issue, [1] presents a swimming fully autonomous robot inspired by the species Lampetra fluviatilis which shows high maneuverability and efficiency, replicating the one of its natural counterpart. Such high performance swimming derives from the recovery of the energy that underwater moving vehicles usually leave in their wake. The Lampetra robot is actuated by an innovative technological solution that exploits the compliancy of a passive backbone to enhance swimming energetic efficiency. With [2] and [3], we shift from swimming to flying, by taking inspiration from insects. Insects, like hummingbirds, are capable of extremely rapid and precise aerial maneuvers. A rapidly growing community of researchers is gathering around the understanding of flapping-wing flight, and its mechanical imitation. At the boundaries of fluid mechanics, structural mechanics and robotics, these new kinds of aerial robots are envisaged to fly in inhabited areas. In this context, a challenging problem concerns the role and exploitation of the wing flexibility for maneuverability, control and actuation efficiency. The focus of [2] is on the effects of the structural flexibility of wings in the flapping flight of a butterfly. In [3], the authors show how flapping aerodynamics can lead to a most-advanced complete autonomous system: the Delfly robot from Delft University. At large, the use of flexible flapping wings is an example of a new emerging topic named `soft robotics' since it aims at solving robotics problems by exploiting the characteristics of soft organs. One of the challenging tasks of soft robotics is to design new actuators capable of reproducing the performance of living animals in terms of integration, rheology, efficiency and control. This perspective is at the foundation of a new generation of robots in our current life, i.e. capable of interacting with humans in a safe manner. The aim of [4]?[6] is to design such a new generation of soft actuators. Mainly taking inspiration from the hydrostats, they report on current works performed in the framework of the EU funded project Octopus, which aims at designing and building an underwater robot inspired by this animal. Beyond their specific scopes, these three articles are emblematic of the bio-inspired methodology, by going from Nature to robotics via mathematical modeling. Sensing is also of interest because inspiration from nature offers new sensory modalities and working principles. The extraordinary performance of supposedly simple animals is a source of wonder and new ideas for researchers. For example, with only a few hundred neurons, a fly is capable of aerial acrobatics beyond what the most sophisticated flying machine can achieve. By exploiting this idea, which consists of extracting simple concepts from neuroethology studies, [7] opens new perspectives in vision, based on the human ability to quickly select intrinsically salient targets in a visual scene: an ability which is fundamental for swift decision-making processes in unpredictable and unknown circumstances. Sensory?motor integration of animals, even the simplest ones, also presents many interesting sources of knowledge for control engineering. The nervous system integrates the information necessary to link perception to action. The active collaboration between roboticists and neurobiologists has already lead to transposition of the functional role of the lamprey's spinal cord into command algorithms for swimming robots (see again [1]). Two other cases of such fruitful collaborations are illustrated in [8] and [9]. These last two papers give a complementary point of view to the bio-inspired approach when applied to higher levels of autonomy. In particular one of the crucial problems of the collaboration between roboticists and neurobiologists is that brains and computers do not use the same principle to encode information. In this regard, [8] presents an interface which encodes sensory data from a humanoid robot into spikes and decodes spikes into motor commands that are sent to the robot. Finally, this issue ends with a biologically inspired navigation system for the mobile rat-like robot named Psikharpax, allowing self-localization and autonomous navigation in an initially unknown environment [9]. This last article represents the highest level (from body to cognition) of bio-inspiration and also completes the picture initiated with the Lampetra robot depicted in the first article. Finally, we are confident that this issue can represent a nice set of new ideas and research references and we really hope that readers enjoy the result of this effort. References [1] Stefanini C, Orofino S, Manfredi L, Mintchev S, Marrazza S, Assaf T, Capantini L, Sinibaldi E, Grillner S, Wall?n P and Dario P 2012 A novel autonomous, bioinspired swimming robot developed by neuroscientists and bioengineers Bioinspir. Biomim. 7 025001 [2] Senda K, Obara T, Kitamura M and Yokoyama N 2012 Effects of structural flexibility of wings in flapping flight of butterfly Bioinspir. Biomim. 7 025002 [3] de Croon G C H E, Groen M A, De Wagter C, Remes B, Ruijsink R and van Oudheusden B W 2012 Design, aerodynamics and autonomy of the DelFly Bioinspir. Biomim. 7 025003 [4] Margheri L, Laschi C and Mazzolai B 2012 Soft robotic arm inspired by the octopus: I. From biological functions to artificial requirements Bioinspir. Biomim. 7 025004 [5] Mazzolai B, Margheri L, Cianchetti M, Dario P and Laschi C 2012 Soft robotic arm inspired by the octopus. II. From artificial requirements to innovative technological solutions Bioinspir. Biomim. 7 025005 [6] Renda F, Cianchetti M, Giorelli M, Arienti A and Laschi C 2012 A 3D Steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm Bioinspir. Biomim. 7 025006 [7] Schiavone G, Izzo F, Simoes L F and de Croon G C H E 2012 Autonomous spacecraft landing through human pre-attentive vision Bioinspir. Biomim. 7 025007 [8] Gamez D, Fidjeland A K and Lazdins E 2012 iSpike: A spiking neural interface for the iCub robot Bioinspir. Biomim. 7 025008 [9] Caluwaerts K, Staffa M, N'Guyen S, Grand C, Dolle L, Favre-Felix A, Girard B and Khamassi M 2012 A biologically inspired meta-control navigation system for the Psikharpax rat robot Bioinspir. Biomim. 7 025009

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods., 13 pages, 8 figures, submitted to Nucl. Instr. Meth. A

The lightning activity is frequent in Hunan, and frequent lightning activities seriously affect the safe and stable operation of distribution network equipment. The statistical analysis of lightning trip-out in Hunan distribution line is made in this paper. According to the current situation of lightning protection in Hunan distribution lines, the differentiated lightning protection management strategy is proposed, which takes into account the ground flash density and special landform factors. And the field application in the past year shows that the effect of lightning protection is good. However, there are also corresponding management and technical problems in the implementation process. The corresponding solution ideas and suggestions are put forward in this paper based on the fully investigation and the experimentation simulation, which has the certain reference value for further improving the lightning withstand level of hunan distribution lines.

Instead of that conventional method of chemical (sanitizer spray) disinfection, this paper introduces a novel idea to fight the attack of microbes causing infections and diseases from the source like lavatory and specially its seat. Here we utilize the germicidal power of ultra violet light ray’s. i.e. Ultra Violet C range of the light spectrum against the viruses (SARS-CoV-2), bacteria (E. coli) and other microbes. This system of disinfection constitutes the UV LEDs placed on the arm above the toilet seat. The range of wavelength is from 200nm to 280nm. Implementation of this technique ensures safety, by human free room with the automatic motion control using Arduino, motor and door locking gadgets. UVC light is very advantageous over other methods as it is eco-friendly, chemical free, non-corrosive, and no hazardous waste is produced and also inactivates viruses by attacking the DNA strands in the microbes ensuring that the replication does not happen further stops spreading of contagious diseases leading to pandemic. Usage of toilet paper and other methods used conventionally could come to an end. Thus an effective idea is derived for the hygiene establishment.

In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisation-based methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU.

The paper researches the features of the rotor dynamics of knife refining machines. The rotor of these machines turns in the pulp. The total imbalance of the refiner rotor consists of mechanical, hydraulic and hydrodynamic components. The mechanical imbalance of the refiner rotor changes due to the plate wear. In the knife refining machines, it is advisable to use automatic balancing devices to compensate for the imbalance of the rotor during operation of the refiner without stopping it. The methodology for calculating the beats of the rotor plate has been developed and tested. The operational cross-knife gaps of the knife refining machines are comparable to the beats obtained. It is recommended to eliminate gaps in the design of the rotor assembly, i.e. use bearings with tensioner and use a stator to make refiner adjustment. The developed methodology for calculating beats can be used in other similar machines, for example, in centrifugal pumps.

The fish processing industry applies a small number of automation technologies compared to other industries. Their number should be increased despite automated food processing problems and hygiene requirements. Automation and implementation of robots can reduce production costs and improve product quality. The issues of designing automation systems for the fishing industry are analyzed. Automatic control systems can control quality of fish and fish products. Automated systems receive, freeze, sort, cut, wash, salt, dry, smoke, press, cool, package and store fish and fish products. Modern technological tools are equipped with automatic control systems. Some of them include specialized automation and robotic units equipped with microprocessor control systems. Application and implementation of automation systems for processing fish are described. Future trends are discussed.

As the coconut palm growers are struggling with the acute shortage of human coconut tree climbers to climb and harvest the coconuts, many are working towards possible alternatives to help them handle this situation. In this study paper we analyse the problems associated with the shortage of human coconut tree climbers in -depth. We also present details of various existing mechanical models available in the market and have not yet solved this issue. Along with this we discuss how robotics and automation could be a possible solution for this entire problem. In this context we discuss about the features of such robotic system and also give suggestions on various unmanned robotic models that can be designed and implemented.

Advances in monitoring technology allow blood pressure waveforms to be collected at sampling frequencies of 250-1000Hz for long time periods. However, much of the raw data are under analysed. Heart rate variability (HRV) methods, in which beat-to-beat interval lengths are extracted and analysed, have been extensively studied, However, this approach discards the majority of the raw data.OBJECTIVE:Our aim is to detect changes in the shape of the waveform in long streams of blood pressure data.APPROACH:Our approach involves extracting key features from large complex datasets by generating a reconstructed attractor in a three-dimensional phase space using delay coordinates from a window of the entire raw waveform data. The naturally occurring baseline variation is removed by projecting the attractor onto a plane from which new quantitative measures are obtained. The time window is moved through the data to give a collection of signals which relate to various aspects of the waveform shape.MAIN RESULTS:This approach enables visualisation and quantification of changes in the waveform shape and has been applied to blood pressure data collected from conscious unrestrained mice and to human blood pressure data. The interpretation of the attractor measures is aided by the analysis of simple artificial waveforms.SIGNIFICANCE:We have developed and analysed a new method for analysing blood pressure data that uses all of the waveform data and hence can detect changes in the waveform shape that HRV methods cannot, which is confirmed with an example, and hence our method goes "beyond HRV".

Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.

Visualization techniques for augmented reality (AR) and mixed reality (MR) are developed well enough to display any data, if need be. This paper deals with a new data visualization technique for efficient product storage and use. The solution proposed herein applies AR visualization. The paper describes an application that displays all necessary data on the screen using AR-markers, and has an option of making them markers complicated and adding three-dimensional models of an object.

In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report data wirelessly and powered perpetually using clean energy. The developed edge device utilizes solar energy harvesting mechanism through photovoltaic (PV) module, this approach will avoid periodical battery replacement and hence, the energy supplied to the sensor mode is not limited anymore. The implemented testbed is based on open-source hardware and software platforms while the simulation environment is based on MATLAB/SIMULINK 2019a. The effects of temperature and solar irradiance on the performance of the developed approach are examined in order to confirm the leverage of the proposed methodology scheme. The lifetime of the developed green IoT device is predicted based on the device’s activities, current consumption, and energy storage capacity. The obtained results showed that the battery lifetime is extended by 38-49% when the edge device runs on an independent power source.

This paper details to an electromagnetic modeling of an extreme ultraviolet (EUV) lithography mask. For that purpose, a modal method based on a spline nodal expansion (MMSNE) is presented. The results obtained using first, and second-order splines as basis functions are compared with those obtained using other modal methods, such as modal method by Fourier expansion (MMFE). The agreement between the results obtained using different methods is very good, and a convergence test is also performed. The spline nodal basis function implemented in this paper is the first step toward the realization of a multiresolution scheme that is expected to perform much more efficiently than conventional schemes.

This paper investigates the seamless mapping of wireless mmWave phase-levels to the optical phase-levels in a seamless digital mmWave-to-photonic converter. We report a non-linear mapping of phase-levels from the wireless to the optical domain which results in digital symbol degeneracy. Our work provides various optimum design guidelines to make such seamless converters practically feasible in digital communication links. The latency and the design complexity of such a seamless converter are expected to be appreciably lower than those of a conventional converter, thereby making it more suitable for 6G communication systems. Although the wireless operating frequency considered in this paper belongs to the mmWave band, all the contributions presented are equally applicable and relevant in the THz wireless band as well.

Under the pressure of energy and environmental protection, we will promote the technological progress and demonstration of electric vehicles, and the construction of charging facilities will continue. Charging facilities planning and orderly charging, as two major research directions of electric vehicle infrastructure, are of great significance for the future development of electric vehicles. The optimal charging of electric vehicles can effectively improve the safe and economic operation ability of distribution network, which is of great significance to its safe operation. Therefore, this paper proposes the outsourcing test experiment and processing of urban electric vehicle public charging network based on 5G and big data. In this paper, through the analysis of the development status of urban electric vehicles, this paper proposes to optimize the charging mode of electric vehicles by combining the charging network forward and backward algorithm. In the outsourcing test experiment, the electrical safety test shows that when the current reaches 1.1-37.1kw: 5000A, when the power factor is 0.8 ∼ 0.9, when the short-circuit current impact is tolerated, the connection device will not affect the breaking operation by contact fusion welding, and the insulation protection will not be invalid. Through investigation and analysis, the satisfaction degree of electric vehicle optimization algorithm is increasing year by year. Through the analysis of the test results, the research in this paper has achieved ideal results and made a contribution to the research of urban electric vehicle public charging network.

Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.

This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.

Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

A review on Performance Based Plastic Design method (PBPD) for RCC building has been presented in this paper with detailed manner. The first is the study of Performance Based Plastic Design method relating with IS code method. And then secondly review includes the research gap that is to apply this method on Irregular Buildings and to observe the efficiency of Design and Cost analysis comparing to IS code recommendations. When a Structure is designed by IS Code (IS 456: 2000) recommendations, it satisfies the strength as well as serviceability standards. But when an earthquake hits the structure, it leads to collapse which leads to loss of life as well as property. A Recently developed PBPD method can be a promising & efficient seismic design approach as it counteract total collapse of structure. This method is becoming popular due to its total collapse prevention. The paper addressed the relevant examination of provisions as specified in the appropriate design standard and software-based finite element analysis conducted by various authors, as well as a review of the problem, in order to reach a meaningful conclusion for the study. Also, a suggestion for the scope of future development is to study the Design as well as Cost efficiency of Performance Based Plastic Design of Various Irregular RCC buildings which will be a new or important aspect of the study. Despite the fact that fewer papers have been published in this area than in others, this report covers a broad range of research efforts and progress into the New Performance based Plastic Design method & seismic behavior of irregular buildings, demonstrating the field’s growing interest.

Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.

As a value-added service that improves the efficiency of online customer service, customer service robots have been well received by sellers in recent years. Because the robot strives to free the customer service staff from the heavy consulting services in the past, thereby reducing the seller’s operating costs and improving the quality of online services. The purpose of this article is to study the intelligent customer service robot scene understanding technology based on deep learning. It mainly introduces some commonly used models and training methods of deep learning and the application fields of deep learning. Analyzed the problems of the traditional Encoder-Decoder framework, and introduced the chat model designed in this paper based on these problems, that is, the intelligent chat robot model (T-DLLModel) obtained by combining the neural network topic model and the deep learning language model. Conduct an independent question understanding experiment based on question retelling and a question understanding experiment combined with contextual information on the dialogue between online shopping customer service and customers. The experimental results show that when the similarity threshold is 0.4, the method achieves better results, and an F value of 0.5 is achieved. The semantic similarity calculation method proposed in this paper is better than the traditional method based on keywords and semantic information, especially when the similarity threshold increases, the recall rate of this paper is significantly better than the traditional method. The method in this article has a slightly better answer sorting effect on the real customer service dialogue data than the method based on LDA.