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The quality of sponge iron produced in the coal-fired rotary kilns at TATA Steel Long Products Limited (TSLPL) is permitted to vary in a small window of 80-83% Fe. In order to adhere to this window, the expected quality in the next hour is predicted, and appropriate actions are taken if changes or rates of change that take place are not in the right direction. Airflow rate through the primary air blower is controlled constantly. For quality prediction and control purposes, the One Step Ahead Adaptive Control and Prediction Algorithm (OSAA) and Grey (1,1) prediction algorithm have been tailored and tuned. The working principle of OSAA and Grey(1,1) are described, and their accuracy is compared. The results show that OSAA performs better than the Grey(1,1). The OSAA is therefore used to predict the changes required in volume of air blown through the primary air blower and also the amount of injection coal. An expert control system has been developed based on allowable windows of control variables.

Chili-based products are increasingly in demand. Realizing this demand, many companies are taking the opportunity to run a chili processing business including turning it into a grind chili and instant chili paste in bottle packaging. The processing of this product goes through several stages, including the process of separating the chilies from the stalks, the cleaning process, grinding the chilies, and ending with the packing work. The main problem is during the process of separating the chilies from the stalks as it is still traditionally practiced by manually separating the stalks from the chilies by workers. It takes a lot of workforce and takes a long time to complete in large quantities. The researchers have carried out a prototype study by designing and building a machine that has been tested and has the ability to separate the chilies from the stalks. The sample of this research was the Sri Azwani Company which had successfully passed the screening by a panel of selected evaluators to obtain the PPRN grant award. The company had encountered problems during the process of separating the bird’s eye chilies from the stalks because it took a while to process 30kg-50kg of chilies for a mass production to produce 530-550 bottles at a time. The machine has a cylindrical main chamber that rotates according to the pre-determined resolution. The machine can process a maximum of 75kg-100kg per day of operation and it will impact the industry in terms of increased production volume, time savings, and cost.

There are many factors that can affect the quality of Refined Bleached Deodorized Palm Oil produced. Every step in RBDPO processing must be mastered very precisely in terms of process conditions such as temperature, flowrate, dose, pressure, etc. This condition can vary from one facility to another and therefore requires the company’s own specifications / rules to implement and integrate all production process facilities until the product is produced. The focus of the study on this particular task is the quality problems faced by companies in the Refinery section where research is carried out using data mining concepts to predict data logsheets that are written every 24 hours by refinery operators. The data can be used as a tool to predict the color quality of Refined Bleached Deodorized Palm Oil (RBPO). The use of Data Mining in this study is to facilitate operators in managing machine settings to achieve the quality required by the company directly. So that the Refinery section does not need to wait for the results of laboratory tests which require approximately 2 hours to find out the RBDPO quality results.

Palm oil is one of the most consumed and produced oils in the world. It can be used as a raw material for various products such as food, cosmetics, as well as a source of biofuel and biodiesel. One of the companies that produces Crude Palm Oil (CPO) is company X. A fundamental mistake that often occurs in making sales decisions for CPO products is inaccuracy in providing sales predictions. So the aim of this study was to evaluate the demand forecasting made by Company X using the Time Series method. Based on the company’s historical data from January 2013 to December 2017, CPO sales forecasting for 2018 is carried out using the decomposition method, moving average, exponential smoothing, and trend analysis as part of the Time Series method. The forecasting results showed that the highest sales is in May 2018 amounting to 2,105,952 kg and the lowest forecasting in December 2018 about 766,667 kg.

PT. XYZ is a company that produces agar powder. This research aimed to minimize waste that happens in the production process of agar powder by using Lean Manufacturing concept with Value Stream Mapping and Simulation Model. The results of this research indicate there is waste such as human potential, inappropriate design, unnecessary, and with the highest waste is in the form of waiting time. It caused by inconsistent of raw material, unresponsiveness worker, lack of tools that help operator, bottleneck process and unscheduled process. Recommendations for improvement are embracing capacity in filtering workstation, equip workstation with timer, and making production schedule. Based on the simulation that have been done shows reduced total lead time of 2.94 days to 2.27 days or efficiency process increased 22.81%.

A system based on the HACCP principles represents an effective tool for poultry processing. This system provides risk identification and corrective actions for it reducing. A low level of metrological assurance of CCP effects on risk identity because it depends on accuracy of measurement information. MSA method for measurement assessment of CCP in poultry production is recommended. The metrological analysis of CCP for technology process is developed. The researches of temperature of cooled poultry meat is traced in real-time production. At the first step with the use of control diagrams the stable measurement process was established. Second step showed the absolute and relative meanings for deviation in measurement process. At the third stage the assessment of measurement deviation was leaded. At the final stage over result grading the top ways of reducing measurement deviation were estimated. The analysis showed that measurement process should be improved. Changes the properties of meat samples is the major du ring temperature measurement. The second place is for measurement repeatability. The measurement method is important, that’s why for reducing the measurement deviation it is recommended to change the method or choose another measuring instrument. The role of mechanic was not recognized. Proposed assessment approach for measurement process of CCP based on HACCP principles in order to identify risks within the food safety control and to minimize technique regulation procedure.

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.

Food is essential for all the human being. In the food we are gained the more number of energy to our body. But now days, Lot of chemical substances that are added in the food. That makes human being to be unhealthy person. Children are also affected due to unhealthy food. In our proposal, we used to find the quality of food using the pH sensor. The pH sensors are used to find the pH value of the food and also find the bacterial contamination of the food. This project is used for the government to find the quality of food in the restaurant and display their result in the server and also check the quality of food and display the output pH value in the message using GSM. In additional, we can also share the location of the restaurant.

The article provides a comparison of the main valuation techniques for a regulated parameter of printability of the offset paper by current standards GOST 24356 and ISO 3783: 2006. The results of development and implementation of a complex test scale for management and control the quality of printed production are represented. The estimation scale is introduced. It includes normalized parameters of print optical density, print uniformity, picking out speed, the value of dot gain, print contrast with the added criteria of minimizing microtexts, a paper slip, resolution threshold and effusing ability of paper surface. The results of analysis allow directionally form surface properties of the substrate to facilitate achieving the required quality of the printed image parameters, i. e. optical density of a print at a predetermined level not less than 1.3, the print uniformity with minimal deviation of dot gain about the order of 10 per cents.

The article describes the task of optimizing the process of sulfuric acid alkylation as part of a group of plants for the production of alkyl benzine. The main methods and approaches to solving this problem from the point of view of real-time optimization (RTO) are presented. A modified method is proposed in the form of a multi-model approach and decomposition of the original optimization problem into local ones with a sequential solution. To test the method, an analytical model of the sulfuric acid alkylation process was used, its evaluation was carried out and the problem of maximizing alkyl benzine was solved using the Hook – Jeeves algorithm in Matlab. The results of the solution were compared with the real historical data of the process and confirmed the operability of the solution.

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".

A mathematical model for fibrous structures using a direction dependent scaling law is presented. The orientation of fibrous nets (e.g. paper) is analysed with a method based on the curvelet transform. The curvelet-based orientation analysis has been tested successfully on real data from paper samples: the major directions of fibrefibre orientation can apparently be recovered. Similar results are achieved in tests on data simulated by the new model, allowing a comparison with ground truth. peerReviewed

Spindle screw pumps are used in numerous different applications and industrial sectors. When selecting a suitable spindle screw pump for a specified application, manufacturers are often confronted with a lack of comparable measurement data for the desired combination of operating conditions and pumping fluid. Consequently, the volumetric and mechanical-hydraulic efficiency of a pump under the operating conditions of the application need to be estimated. In this context, this paper discusses the application of similarity for three-spindle screw pumps and introduces a similarity based efficiency model. The model is validated by means of a measured pump characteristic at different operating conditions varying pressure, rotational speed and the viscosity of the pumping fluid. The validation results prove that a pump characteristic can be represented over a wide operating range based on similarity. An estimation of the volumetric and the mechanical-hydraulic efficiency at a changed viscosity is achieved with high accuracy. Furthermore, a new approach to monitor and to evaluate manufacturing uncertainty based on the model parameter relative gap is presented. Finally, the paper gives an outlook on future experimental investigations at TU Darmstadt on model series of three-spindle screw pumps containing pumps of different displacement volume.

Adequate thermal protection for passengers travelling on-board vessels in cold climate regions, such as that provided by thermal protective immersion suits (TPIS), enhances passenger survivability in emergency situations, in particular those requiring the abandonment of the vessel. As emergency abandonment is a time critical process, it is essential to consider the time required to correctly don the TPIS. Testing standards, such as the International Maritime Organization guidelines, require that TPIS must be able to be donned within 2 minutes. Unfortunately, current practices quantifying donning times are questionable and so there is a limited evidence base that reliably quantifies donning times required by typical passengers. This paper presents a test procedure designed to reliably quantify the time required by test subjects to don the TPIS. Furthermore, the procedure assesses the donning correctness – a TPIS that is incorrectly donned is unlikely to offer appropriate thermal protection. The paper will also discuss the deficiencies in current practices to assess required donning time.

The ability to label and track physical objects that are assets in digital representations of the world is foundational to many complex systems. Simple, yet powerful methods such as bar- and QR-codes have been highly successful, e.g. in the retail space, but the lack of security, limited information content and impossibility of seamless integration with the environment have prevented a large-scale linking of physical objects to their digital twins. This paper proposes to link digital assets created through BIM with their physical counterparts using fiducial markers with patterns defined by Cholesteric Spherical Reflectors (CSRs), selective retroreflectors produced using liquid crystal self-assembly. The markers leverage the ability of CSRs to encode information that is easily detected and read with computer vision while remaining practically invisible to the human eye. We analyze the potential of a CSR-based infrastructure from the perspective of BIM, critically reviewing the outstanding challenges in applying this new class of functional materials, and we discuss extended opportunities arising in assisting autonomous mobile robots to reliably navigate human-populated environments, as well as in augmented reality., 30 pages, 8 figures. This paper is a very interdisciplinary topical review on the use of Cholesteric Spherical Reflectors to make fiducial markers-- visible to robots but not humans-- to link Digital and Physical twins. The authors are from fields including Design, Materials Science, Security and Computer Science, and Physics

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.

Uncertainties that exist in nature or due to lack of knowledge have been widely recognized by researchers and engineering practitioners throughout engineering design and analysis for decades. Though great efforts have been devoted to the issues of uncertainty quantification (UQ) in various aspects, the methodologies on the quantification of aleatory uncertainty and epistemic uncertainty are usually logically inconsistent. For instance, the aleatory uncertainty is usually quantified in the framework of probability theory, whereas the epistemic uncertainty is quantified mostly by non-probabilistic methods. In the present paper, a probabilistically consistent framework for the quantification of both aleatory and epistemic uncertainty by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is outlined. The framework is then applied to the backward issues of uncertainty quantification. In particular, the uncertainty model updating issue is discussed in this paper. A numerical example is presented, and the results indicate the flexibility and efficiency of the proposed PDEM-COM framework.

Life Cycle Assessment (LCA) is increasingly applied to evaluate the environmental performance of buildings. However, current tools for building LCA require detailed information not available in the decisive early design stages. As a result, LCA is usually applied as post-design evaluation and not used to improve the building design. The goal of this paper is to adapt the method of structured under-specified LCA to the Swiss context and implement it in a design-integrated tool. The users of the tool should be able to get a complete estimation of the life cycle impact based on very few inputs, such as building type, intended use and structural system. In addition, the tool should allow to replace these assumptions with more detailed information step by step throughout the design process. The paper describes the development of a structured database and a parametric tool. Furthermore, it exemplifies the intended workflow during the design process on a building design. The presented approach can be scaled up and adapted to the needs of other national contexts in the future. It facilitates environmental performance optimisation of buildings and supports making use of the big potential the building sector has regarding contributing towards climate action (UN SDG 13)., IOP Conference Series: Earth and Environmental Science, 588, ISSN:1755-1315, ISSN:1755-1307

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.

With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.

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.

With the environmental pollution and the shortage of oil resources becoming more and more serious, the development and application of new energy vehicles have attracted more and more attention. Engine is an important part of new energy vehicles, and its performance has a great impact on the vehicle. Compared with traditional industrial motors, new energy vehicle engines have higher requirements on power density, and the improvement of power density poses new challenges to the design of motor cooling system. The purpose of this paper is to study the engine cooling device of new energy vehicles and improve the overall performance of the vehicle. The main research content of this paper is to lay a foundation for the theoretical basis of the engine cooling device, elaborate the working principle of the motor cooling system and the loss of the motor in operation. Then, the heat dissipation system of permanent magnet synchronous motor based on heat pipe is studied experimentally. Aiming at the problem of only considering the temperature rise and ignoring the pressure loss in the flow channel design, a flow channel design method considering the motor temperature rise and the flow channel pressure loss is proposed, and the motor flow channel is optimized. The test results show that the maximum temperature rise at the end is close to 16.56 °C, which is in good agreement with the simulation results. It shows that the heat pipe based heat dissipation system can effectively reduce the temperature rise of motor winding, which provides a new idea for the heat dissipation design of permanent magnet synchronous motor

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.

Comparators play a significant role in the semiconductor industry and have become indispensable in the design of ADC. The delay and energy consumption are two important indicators of the comparator. Many designs have been made to reduce the delay and energy consumption, such as separated gata-biasing cross-coupled transistors for a new latching stage, and pMOS is used to replace nMOS in comparators. This paper analyzes the working principle of the proposed comparators designed for different needs reported on different papers. It compares their simulation results about key data such as delay and energy consumption.

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.

For industrial-grade manipulators, the study of trajectory tracking control issues provides an important guarantee for accurate and safe work. Therefore, the trajectory control input driving torque can meet the requirements of the robot arm to accurately track a given target trajectory, and the process of building a decision tree is a process of dividing the feature space. For a given training data set, a set of if-then is summarized the rule of. Based on this, this paper launches the research of multi-degree-of-freedom manipulator joint trajectory tracking control method based on decision tree. Based on the established kinematics and dynamics model of the manipulator, this paper uses a proportional-integral-derivative (PID) sliding mode controller based on the sliding mode surface of the manipulator to perform the trajectory tracking control of the end of the manipulator, and the simulation results of the improved sliding mode control are compared with the simulation results of the improved sliding mode control. The simulation results of the PID controller and the traditional sliding mode controller are compared. This paper finally verifies the effectiveness of the proposed new sliding mode controller based on the expanded state observer through the experimental platform. The speed and chattering problems of the trajectory tracking at the end of the manipulator are better than those of the controller on the experimental platform. Finally, this paper adopts the sliding mode variable structure control strategy combining the double-power reaching law and the improved terminal sliding mode surface to study the trajectory tracking control of the planar two-degree-of-freedom manipulator.

Sleep staging is an important process for detecting sleep quality and diagnosing sleep disorders. However, traditional sleep staging is a labor-intensive task, and it is prone to subjective errors. Therefore, this paper innovatively proposes an automatic sleep staging model based on single-channel EOG—CRNN-HMM. The CRNN-HMM in this paper combines Convolutional recursive neural networks(CRNN) and hidden Markov model(HMM). The main idea of this model is to use CRNN to automatically extract features from EOG, and send the feature signals to a variant of RNN, Bi-directional Long Short-Term Memory(BiLSTM), to mine the dependencies between sleep stages and realize automatic staging of sleep data. Finally, a Hidden Markov Model is used to convert the prior information of the sleep phase of the adjacent EOG cycle in order to improve the classification performance of S1, thereby improving the classification performance of CRNN. The simulation results show that the overall accuracy of the model on the CAP-Sleep data set reaches 95.0%, which proves that the model can provide a way for the evaluation of sleep quality.

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%.

Event detection is an important foundation of non-intrusive load monitoring algorithm. In this paper, the common household appliance load events are classified, and a new triple-threshold event detection algorithm is proposed aimed at solving the problems of false detection and missing detection in the practical application. Firstly, a low power threshold is used to realize high-sensitive detection of the load events, and secondly the detected events are spliced according to the time threshold to get the complete events. Thirdly, the high threshold is used to discriminate the complete event set to filter out the disturbance caused by load fluctuation. Finally, the results are modified with a correction logic. The test results carried with static data show that, the algorithm proposed in this paper is more accurate for positioning the time of putting into and cutting off load, which is conducive to improve the accuracy of transient interval interception of load events, and has advantages in detecting slow rising load events. In addition, the algorithm proposed in this paper has a small amount of calculation, which can meet the requirements of application in the hardware of smart meter.

Objective and reasonable evaluation is the basis of power-generating unit optimal operation, but evaluation method based on single weight can’t adapt to the high accuracy requirement of comprehensive evaluation. In order to achieve the accurate evaluation of power-generating unit operating reliability, this paper firstly calculates subjective weight and objective weight of target attribute by analytic hierarchy process method and entropy weight method. It calculates the associated weight of target attribute by grey relational degree method, and it obtains the linear combined weight by relaxation factor. Then, the power-generating unit operating reliability is evaluated by improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and the evaluation result is obtained. Finally, the relaxation factor is continuously evaluated from 0 to 1 to perform sensitivity analysis. This paper evaluates the operation reliability of nine wind turbines in a wind farm, and the evaluation result is closer to actual operation state of wind turbines. This method has high reliability and practical value, and it provides a new technical means for making a reasonable maintenance plan of wind turbines.

The paper reported the temporal feature analysis method describing for automata models of computation systems. The method is based on from source algorithm to modified algorithm expanding makes it possible to take characteristics of work process related to execution time measurement. The paper considers the transition method from source algorithm representation to modification a final state machine with additional states providing registration of temporal features. The paper demonstrates method usage example on computer system authorization algorithm, approves an algorithm complexity for features registration O(n) for n-states automata algorithm. The method can be used for a large class algorithm, but is recommended to apply it to an algorithm, separated on procedure thus mean of commands amount should be more than 500 for more time measurement accuracy.

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.

Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).

In recent years, multiple measurement systems fusion technologies have developed rapidly. Ultrasonic ranging is paid much attention because it has the advantages of directly measuring short-distance targets, high longitudinal resolution, and a wide application range. However, when the ultrasonic distance measurement is carried out by assembling the circuit board, it is found that the switch is triggered multiple times, and the measurement data is inaccurate. This paper proposes a new method that adding debounce to improve the accuracy of measurement. Besides, Some suggestions are provided to decrease the operation difficulty in the actual measurement process. Based on the experimental result, the accuracy and efficiency of ultrasonic distance measurement are greatly improved via this method proposed in this paper. By the way, the single-chip computer simulation technology simulates the ranging phenomenon in the experiment.

Traditional control instruments and DCS control systems are limited by shortcomings such as single control function, independent controlled elements, inability to close-loop control on site, and ineffective communication. Aiming at these problems, this paper proposes a kind of industrial furnace edge controller which has the functions of acquisition, calculation, control, storage, output and communication in one. The edge controller structure, edge computing and the control method applied to edge computing are designed. The fuzzy PID control method is compared with the conventional PID control method by taking the temperature control of resistance furnace as an example. The simulation shows that the control effect of fuzzy PID on resistance furnace temperature is obviously better than that of conventional PID. The test results also show that the performance indices for the fuzzy PID control are better, which again proves that the fuzzy PID control outperforms. The research results of this paper are of reference value for engineering applications.

In recent years, with the improvement of Internet of Things technology and the continuous improvement of economic level, people’s consumption consciousness has also changed, especially for the packaging of networked products put forward higher requirements. This paper mainly studies the visual expression of networked product based on Internet of Things technology. Starting from the increasing influence of Internet electronic consumption in China’s economic consumption system, this paper deeply discusses the systematic product logistics packaging design under the Internet consumption form. This paper analyzes and discusses the differences between Internet consumer commodity packaging and physical consumer commodity packaging in functional orientation, added value of packaging, systematic visual design and other aspects.

Dwelling as an alternative to cure and isolate confirmed positive or asymptomatic people of COVID-19 becomes an essential place. However, it is necessary to ensure no physical contact between dwelling users since COVID-19 can be transmitted through droplets. Preventing and controlling the transmission is achieved by inserting transitional space between users, activities, or programs. The idea of transitional space is determined from Rumoh Aceh, an adaptive vernacular design that provides a boundary between public and private zones to limit access to strangers. This paper aims to translate space configurations of Rumoh Aceh as local wisdom to break the chain of COVID-19 transmission by making transitional space, a separation between confirmed positive or asymptomatic and healthy people. The data is obtained from the observation of three Rumoh Aceh in Banda Aceh and Aceh Besar. The space configurations are translated into five types of threshold space, promoting social distancing between users, providing cleaning space for personal hygiene, giving atmosphere for self-isolation, having natural ventilation features, and daylight exposure. Then, these types are explored and adapted in a contemporary dwelling design. As a final translation and exploration, this paper provides strategies and design recommendations for a threshold space in a contemporary dwelling design. The strategies and recommendations are explored and adapted in a 60 square meter house plan.

The paper describes a machine vision system for SMART identification of objects. In the introduction, this system is analyzed from a theoretical point of view. Advantages and possibilities of its use are emphasized. The following section presents our research workplace, where the machine vision is located. It involves two identification stations, one of them with 3D measurement and the second is a 2D multi-spectrum with several types of lighting. One part of this section describes the workplace in 2D view and equipment of vision system situated in this laboratory. The third part examines the possibilities of character identification by this system. The novelty of the paper is presented experiment with different wavelengths of light (RGB, UV, AM, W, infrared, far-red). The result of experiment is the most suitable light for characters identification by vision system.