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Livestock plays very important economic, social and cultural roles in the well being of rural communities across the world. Quality environmental conditions, automation and monitoring are the key necessities of running a good and profitable livestock farm. Air quality, temperature of the surroundings and humidity play a major role while deciding the fan speeds of the exhaust System used in all aspects of livestock farming. Another important part of livestock production is increasing incubation speeds of eggs by performing artificial incubation. It is a requirement to maintain the temperature at a constant value in this system. This paper describes two mutually exclusive Fuzzy Logic algorithm-based systems to automate the exhaust system and an artificial egg incubator. The other important part of a livestock farm is production of milk and milk products. It is required to monitor the health of cows by overseeing their activities at any point of time. This can be done by determining and monitoring the activities performed by the cow. This paper describes a simple Deep Learning Model to classify the activities of a cow broadly as standing, walking or grazing. The Exhaust and the Incubator system are controlled and monitored using Internet of Things (IOT) System using a native web application developed using the Flask framework.

Since from ancient times weight of bullock cart is concentrated on the neck of the bullock reducing its efficiency. This paper presents various design of the yoke structure for the cart and its analysis whose aim is to reduce the stress acting on the single point i.e. on the neck of the bullock. The stress needs to be distributed over the entire body of the bullock instead of concentrating only on the neck. This paper gives an idea of how the stress is distributed on the bullock and the deflection of the yoke corresponding to the load. The design iterative process is based on the presently used bullock cart yoke structure. This paper also includes the design concepts which provide cushioning at the contact of the wooden frame to the bullock. The best design is then selected based on the structural behaviour for particular boundary and loading conditions. The modelling is done in SOLIDWORKS 2017 software and simulated/analysed using ANSYS 18.2 software.

Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.

Power generation around the world is changing dramatically as a consequence of the demand to lower greenhouse gas releases and present a mix of power supplies. Energy storage technology is considered to be the fundamental technology to address these challenges and has great potential. This paper presents the current development and feasibilities of compressed air energy storage (CAES) and provides implications for upcoming technology advancement. The paper introduces various primary categories of CAES (Advanced Adiabatic-CAES, Liquid Air Energy Storage and Supercritical CAES). Compared with other energy storage technologies, CAES is considered a fresh and green energy storage with the distinctive superiorities of high capacity, high power rating, and long-term storage, and shortcomings of low power density, high transportation losses, and geological restriction. CAES is regarded as a promising technology that is able to be applied in renewable energy production, cogeneration, and distributed energy and microgrid systems. It’s also considered to be integrated with other technologies, such as renewable energy, gas turbine, solid oxide fuel cells, and other systems in the future.

With the improvement of people’s awareness of environmental protection in recent years, the related problems of water pollution treatment have gradually come into people’s view. As the source of life, water accounts for a huge proportion in our lives, but at the same time, water pollution is quietly spreading in places we don’t know. The continuous discharge of heavy industrial wastewater, agricultural wastewater and domestic sewage leads to increasingly serious water pollution. Sewage treatment(ST) is imperative, and its social benefits are huge, but the corresponding cost is high, and the return on investment is low. Traditional ST methods can not load large-scale ST. How to carry out ST based on artificial intelligence(AI), build ST plant control system, and make ST enter the era of automation is the problem to be solved. The purpose of this paper is to put forward the reform of control system for ST plant based on AI, apply AI into ST, and realize the automation and precision of ST plant. This paper mainly uses the fuzzy self-tuning PID control system algorithm, through the analysis of ST control object, analysis of fuzzy self-tuning PID controller design to complete the ST control system settings. In this paper, the literature review method and data analysis method are used. By collecting relevant data, the control system of ST plant is constructed to simulate ST, and the real-time data of ST is analyzed. The traditional PID control and fuzzy self-tuning PID control are compared. The experimental results show that the wastewater treatment plant system based on AI input, in the aspect of wastewater treatment, the concentration of COD and BOD in the treated wastewater are reduced by a certain proportion, the dissolved oxygen content in the wastewater reaches about 2.0mg/l, which meets the national discharge standard, and its rising time is reduced to 25 seconds, and the adjustment time is saved by 50 seconds.

The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

With the rapid development and improvement of information technology, information system construction has new requirements and goals. Cloud computing technology is an inevitable choice in the construction of information system. By taking cloud computing technology as the basic structure framework, the phenomenon of data information island can be broken. The purpose of this paper is to study the application of cloud computing in the construction of information system. Taking the management mode of university campus as an example, based on the research of cloud computing technology, virtual machine technology and Internet of things technology, this paper discusses the data resource management algorithm of smart campus. Aiming at the scheduling model, strategy and research objectives of cloud computing resources in data center, this paper proposes a method of initial allocation and adaptive dynamic scheduling of virtual machine resources. According to the multi-dimensional vector characteristics of virtual machine, this paper proposes a resource allocation algorithm based on multi-objective evolution, and gives four important strategies. The experimental results show that the four advantages and strategies can effectively reduce the number of virtual machine migration and reduce the adverse impact on the overall performance of cloud computing core network.

Considering the problems of low communication rate and large communication delay of the traditional single-lamp monitoring system for airfield lighting based on narrow-band power line carrier communication technology, a solution of single-lamp monitoring system for airfield lighting based on broadband power carrier communication technology is proposed. Firstly, this paper has made a comparison of advantages and disadvantages between narrow-band power line carrier communication technology and broadband power line carrier communication technology, and briefed the problems faced by traditional single-lamp monitoring system based on narrow-band power line carrier communication. Taking technical characteristics into consideration, broadband power line carrier communication technology is selected for the development of a new single-lamp monitoring system for airfield lighting. Secondly, this paper articulated the design and development process of new airfield lighting single-lamp monitoring system based on broadband power line carrier communication technology, from the formulation of the overall architecture scheme of the system to the realization of local convergent equipment, and focused on the analysis of hardware and software development process of single-lamp monitoring module. Finally, this paper set up a test environment, and verified the performance index by experiments. The test results show that the system meets the design index requirements, can improve the performance of the single-lamp monitoring system for airfield lighting, effectively solves the problems faced by the traditional system, and can further meet the needs of the airport for airfield lighting monitoring and control.

Conveyor belt transfer is a widely used transportation means in industry and agriculture, with the help of the robot arms the workpiece on the belt can be picked and placed, replacing human sorters for production lines work. The position and orientation of the workpiece are important for grabbing by the robot arms. The goal of the paper was to investigate the acquisition of the position and orientation of the conveyor belt workpiece by means of the camera video overhead looking down the belt. The proposed method is the inter frame difference in nature, using the conveyor belt background as the first frame, but the other frames were not used wholly as usually, only an ROI all around the conveyor belt in the camera video was chosen, and the inter frame difference was carried out in the ROI. The ROI was of the same width as that of the belt in the video which was known in advance, while the length of the ROI was arbitrary, so one pixel in the frame was scaled to the actual length conveniently. Every read frame behind the background was computed the difference with the background in such ROI, and the four vertexes coordinates of the rectangle workpiece image on the belt were obtained when it passed the ROI, and then the distance apart from the right belt boundary was calculated due to the proportional relation between the width of workpiece and that of the ROI. Two kind workpiece orientation on the belt toward the left and right were judged using the same obtained four vertexes coordinates by means of Euclidian length, and the tilt angle was calculated by arc tangent function in favour of two narrow sides of rectangle workpiece grab. The actual test showed that the method of obtaining the position and orientation of workpiece on the belt proposed in the paper could be realized correctly.

Large scale Neural Network (NN) accelerators typically have multiple processing nodes that can be implemented as a multi-core chip, and can be organized on a network of chips (noise) corresponding to neurons with heavy traffic. Portions of several NoC-based NN chip-to-chip interconnect networks are linked to further enhance overall nerve amplification capacity. Large volumes of multicast on-chip or cross-chip can further complicate the construction of a cross-link network and create a NN barrier of device capacity and resources. In this paper, this refer to inter-chip and inter-chip communication strategies known as neuron connection for NN accelerators. Interconnect for powerful fault-tolerant routing system neural NoC is implemented in this paper. This recommends crossbar arbitration placement, virtual interrupts, and path-based parallelization strategies in terms of intra-chip communications for the virtual channel routing resulting in higher NoC output at lower hardware costs. A lightweight NoC compatible chip-to-chip interconnection scheme is proposed regarding to inter-chip communication for multicast-based data traffic to enable efficient interconnection for NoC-based NN chips. Moreover, the proposed methods will be tested with four Field Programmable Gate Arrays (FPGAs) on four hard-wired deep neural network (DNN) chips. From the experimental results it can be illustrate that a high throguput can obtained effectively by the proposed interconnection network in handling thedata traffic and low DNN through advanced links.

The major challenges, which come across face recognition system, are to find the age and gender in 2D/3D image of the person specifically in cloud environment. This paperis centered on face detection with MAC (Media Access Control) and biometric technology. Face scanning along with machine’s MAC address and biometric technologies has been shown to improve security controls. Face recognition can be used to search and label users and their assigned machines for sensitive purposes. Following that, it was stored in a specific database with their unique ID. In addition, the verification process has begun by comparing the models in the database. Face scanning along with speech and biometric technologies is used to improve security controls. Face recognition system may also be set up in high security machines to improve protection by allowing only registered individuals or others users. Related strategies for determining the age and gender and 2D/3D image from a specific picture are explored, as well as several modern methods for preserving protection. In this paper, the full model is explored independently with security implemented in cloud environment. The proposed model of the paper provides the integrated security features using MAC address of machine and face recognition of the machine user.

In view of the wide variety of telecom packages and the difficulty of adapting to the needs of users, this paper introduces a recommendation model for telecom packages based on deep forests. This paper first analyzes the telecom package data, and then optimizes the deep forest according to its characteristics such as discrete, continuous attribute interleaving and high coupling characteristics, including the use of decision trees to discretize continuous features and design continuous window sliding mechanism. These methods can improve the ability of deep forest combination high coupling features. Finally, the model optimization measures were verified by detail experiments. The experimental results show that the optimized deep forest can be applied to the telecom package recommendation field. Compared with other shallow models and unoptimized deep forest models, the deep forest model has increased the F1 score by 5%; after adjusting the deep forest hyper parameters, the F1 score can be increased by 2%.

With the rapid development of the information technology industry, the demand for data centers is gradually expanding. As the foundation of carrying many information technology services, data centers have been built and put into use on a large scale. At the same time, the characteristics of huge energy consumption are more obvious. Due to the defects of low utilization rate, heavy pollution and limited reserves of traditional fossil fuels when supplying energy for data centers, the development of green renewable energy has become an inevitable demand. This paper studies the key technologies involved in supplying power to DC power supply data center with renewable energy, and makes a case to compare the DC power supply system and the AC power supply system. This paper also pays attention to the technology of energy storage in data center, and makes an analysis to prove the advantages of energy storage data center. In addition, this paper studies and summarizes the application of multi-station integration technology and other issues.

The parallel connection of IGBTs has been being applied in high power neutral point clamped (NPC) three level converters. This paper investigates the impact of gate parameters (gate resistor and capacitance) on dynamic current imbalance of parallel connected IGBT for NPC three level converter. A gate parameters calculation method is proposed in the paper, and the delay time and collector current difference can be analysed quantitatively. Experimental results have shown the effectiveness of the method.

the paper introduces IRS to assist offloading, and the propagation Environment can be intelligently changed by changing the reflection unit of the IRS, This article proposes an IRS-assisted MEC power distribution Internet of Things system, and studies the gain effect of IRS in the MEC system. In this system, the single antenna equipment can choose to unload a small part of its computing task to the edge computing node of the distribution Internet of things through the multi antenna access point with the help of IRS. In this paper, the delay minimization problem of the whole system is established, the DNQ reinforcement learning algorithm is used to solve the problem, which can effectively change the coverage of smart substations.

A torsion vibration isolator composed of oblique springs with high-static-low-dynamic stiffness (HSLDS) is proposed to attenuate the transmission of torsion vibration along the shipping shaft in this paper. It is good at in low frequency vibration isolation as it can significantly reduce the resonance frequency of the system with the same load capability. Firstly, the model of HSLDS torsion vibration isolator is introduced in this paper. Secondly, the non-dimensional torsion stiffness is formulated using mechanics theory, and the HSLDS characteristic of designed torsion vibration isolator is verified. Finally, the torque transmissibility is analyzed using the Increment Harmonic Balance (IHB) method, and the effects of the system parameters on it are analyzed. The results show that the resonant frequency increases accordingly as the stiffness ratio and the excitation torque are increased. However, the peak value of the torsion transmissibility is decreased as the damper ratio increasing.

As a large, complex system with wide distribution and high real-time, the safe, stable and reliable operation of the power system is inseparable from the power automation system. In this paper, an in-depth study is conducted on the key issues of this solution in the process of software cross-platform integration around the EPRI graphic center method, combined with the power data interface standard. The dispatch center graphics system and the dispatch center power graphics application method are discussed in detail in the paper. In addition, the paper also explains the online calibration system for relay protection ratings with smart grid dispatching technology support system. The methods and sample systems in this paper have been practically applied in large scale power grid dispatching centers, providing a reliable guarantee for the safe and stable operation of the power grid.

The rapid development of the high frequency power conversion techniques makes great demands on the methods that can reduce the execution time of the program effectively. This paper is aiming at reducing the execution time of the program in several aspects such as sampling, complex expressions, and so on. As one of the most widely applied methods, reducing the execution time of the program at the cost of the memory space is adopted in this paper. Furthermore, in order to confirm the feasibility and superiority of programs that are proposed in this paper, they are compared with other programs that can realize the same function in terms of the execution time.

This paper describes the process of developing a simplified methodology for furnace aerodynamics during the development or modernization of combustion schemes with direct-flow burners. This technique is based on the use of numerical modeling of air movement and turbulence phenomena in the furnace volume and allows for a relatively short period of time to analyze a large number of options for the burners and nozzles location. This is its advantage in comparison with the use of experimental modeling or numerical simulation with combustion when analyzing a variety of schemes. The model was developed on the basis of validated results of combustion processes numerical simulation in the K-50 boiler furnace. The paper presents the results of calculations performed for several variants of the simplified methodology. For further use, the option that best corresponds to full-scale studies taking into account the fuel combustion process has been selected. The main states of the methodology are formulated.

In this paper we develop a mathematical model of disease transmission dynamics. Although some vaccines for some infectious diseases are available, there are some cases where handling new emerging infectious diseases, such as COVID-19 pandemic, is still a difficult problem to handle. Preventive actions, such as wearing masks, distance guarding, frequent hand washing, and others are still the most important interventions in handling the transmission of this disease. Recently, several countries have allowed the use of convalescent plasma transfusion (CPT) in the management of moderate and severe COVID-19 patients. Several early studies of this use have yielded prospective results with reduced mortality rates. A recent work also shows that using a simple discrete mathematical model of CPT could reduce the outbreak of disease transmission, in the sense of reducing the peak number of active cases and the length of the outbreak itself. In this paper, we use a continuous SIR model applied to COVID-19 pandemic data in Indonesia to address an important question whether convalescent plasma transfusion may reduce the transmission of the disease.

As a component of the Internet of things, high-voltage cables are the power supply infrastructure for the modern development of cities. The operation experience shows that the high-voltage cable has been broken down many times, due to the defective operation. At present, due to the limitation of detection technology, the research on detection and identification of defects in high-voltage cables is progressing slowly. Therefore, a new DR technology based on X-ray digital imaging is proposed in this paper to realize real-time detection of defects in the semi-conductive buffer layer of high-voltage cables, and intelligent detection of DR images of high-voltage cables by using image depth processing technology to realize intelligent identification of defects in the buffer layer of power cables. The results show that using the new DR technique proposed in this paper, the accurate and intuitive DR image of high-voltage cable can be obtained quickly, and the intelligent identification of defects can be realized.

Steganography can be described as approach of masking an undisclosed message with a normal message which is known as the Carrier message signal. DSP techniques, such as LSB encoding, have historically been implemented for secret information hiding. Utilization ofsteganography functions of deep neural networks for voice data is something this paper will present. This paper also demonstrate that the steganography techniques suggested for vision are less suitable for speech signals this paper present a implementation technique that involves the use of ISTFT and STFT as differentiablelayers in the network. Empirically, the efficacy of the proposed methods based on multiple datasets of speech should be demonstrated and the outcome are examined quantitatively and qualitatively. Using of multiple decoders or a single conditional decoder helps to hide multiple signals in a single carrier signal. Finally, under various channel distortion situations, this model Qualitative studies indicate that human listeners cannot detect changes made to the carrier and hence the decoded messages are highly intelligible.

Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor feature interpretability, this paper proposed a hybrid feature selection model based on machine learning and knowledge graph. By the idea of hybridization, this model used supervised learning algorithms, unsupervised learning algorithms and knowledge graph technology to model from the perspective of data features and text features. Firstly, the data-based feature weights were obtained through the machine learning model, and then the text-based weights were obtained by using the knowledge graph technology, and the weight sets are combined to obtain a feature matrix with good explanatory properties that meets both the data and text features. Finally, the case analysis proves that the method proposed in this paper has good effects and interpretability.

This paper presents a design and development of an Ankle Foot Orthosis (AFO) printed with a multi-extrusion Fused Deposition Modelling (FDM) printer. AFO is crucial in the healing of patients with ankle joint displacement. It helps to heal the foot easily. The model is designed to print with dual materials such as flex and polylactic acid (PLA). The critical problem in building a prototype is to obtain excellent bonding properties between the layers of different materials. The Flex material is softer than the PLA and both have different melting temperatures. Thus, successfully printing with an excellent adhesive is the key concern in multimaterial applications. The paper also presents the simulation results of an AFO to justify the mechanical properties and required materials for sustainable development.

This research designs an absolute-value detector with the function of threshold comparing. Specifically, it is an essential device in the spike detection of the brain-machine interface. The optimized design in the research can accomplish the main functions in spike detection and has good performance in both delay and energy consumption. It comes up with two types of design at the beginning. To make the design reliable and comprehensive, it decides to discuss both methods in this paper. The first design is using a full adder, multiplexer and comparator. The concept of its logic circuit is adding the logic one to the input when the given input data is negative, keeping the original information as the given input data is positive. To achieve the function of adding, this study chooses the full adders. The primary purpose of using multiplexers is to select from the processed input and original input, and the choice depends on the most significant bit (MSB) of the input data. To compare the absolute value of the input data with a given threshold, this research used a multi-bit comparator. The second design is based on the fundamental algorithms of calculating total numbers. It indicates that this study can operate it with the threshold value through a subtractor when the input is negative. On the contrary, an adder can be used when the information is positive. Based on the concept of logic optimization, this study chooses to use the only subtractors, and it just needs to focus on the borrow bit, which can indicate the more significant number. By connecting the MSB of the input with the subtractors through XOR gates, the selection can be achieved without using any multiplexer. In the process of removing and replacing the devices, it reached the optimization of the design. Then, this paper compared the minimum delay by calculating each stage’s size and finding that the second design is better. Finally, based on the dual design, this essay computed the energy consumption in the circuit and implement VDD optimization to obtain the minimum energy.

There are many factors affecting insulator leakage current of transmission line, resulting in low accuracy of leakage current monitoring. This paper designs a monitoring method for insulator leakage current of transmission line under typical environmental conditions. The influence of different factors on the accuracy of leakage current monitoring is analyzed. Based on the fuzzy mathematics theory, the insulator operating characteristic quantity is analyzed, and the uncertain factors in the insulator operating characteristic quantity are calculated, so as to realize the insulator leakage current monitoring of transmission line under typical environmental conditions. The example analysis shows that the leakage current monitoring accuracy of the method studied in this paper is high in the case of no arc, local arc, adjacent flashover, pollution and saturated environment, which proves the effectiveness of the method studied.

Fault-tolerant design of cache is a key aspect of highly reliable processor design. In this paper, based on the key metrics in Cache architecture design: reliability, power consumption, latency and area, we divided the related research into two categories: one is to maximize reliability with guaranteed latency, power consumption and area, the other is to minimize latency, power consumption and area loss while ensuring fault tolerance reliability. Based on the classification, by analyzing different studies of Data and Tag in Cache, this paper gives the characteristics of these methods and the future development trend.

Dissolved Gas Analysis (DGA) for oil samples has been the most widely used diagnosis tool for transformer condition assessment for many years. However, DGA use to oil-filled transformers with a voltage class up to 100 kV. The aim of this paper is to address the issue of DGA interpretation to oil-filled transformers with a voltage class of 10 kV. This paper will present DGA tests results from 57 power transformers and will propose a maintenance decision making procedure using the IEC 60599-2015 Ratio Method, IEEE Std C57.104-2008 include Dornenberg Ratio Method and Rogers Ratio Method, and Russian Std CTO 56947007-29.180.010.094-2011 and Russian Std RD 153-34.0-46.302-00.

Permissible region (PR) strategy has been used successfully to alleviate the ill-posedness of the X-ray luminescence computed tomography (XLCT) reconstruction problem. In the previous researches on the permissible region strategy, it is obvious that permissible region strategy can solve the reconstruction problem efficiently. This paper aims to research the performances of four types of permissible region extraction strategies, including a permissible region manually extraction strategy, a permissible region extraction strategy with a priori information of the surface nanophosphors distribution, a permissible region extraction strategy based on the first-time reconstruction result and a precise permissible region extraction strategy. In addition, some heuristic conclusions are provided for the future study in this paper. Fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct in this paper. The numerical simulation experiments and physical phantom experiments are setup to evaluate and illustrate the performances of the four different types of permissible region strategies.

Abstract.This paper aims at the high efficiency of field balancing for turbo-generator with large capacity currently, and introduces the strategies and key techniques of the rotor system balancing with practical cases of power plant in field. The acquisition, analysis and former processing of the original vibration data for balance calculation are included. Furthermore, they involve complete measuring points and conditions, accurate judgment for the types of unbalance exciting force and selection of stable vibration data, all these could reduce the blindness of balancing effectively. The strategies and techniques also contain the determination for axial plane of unbalance by the modal method, then the optimal steps and the plane of adding weight are chosen during the implementation of balancing. Besides, this paper also introduces the analysis and selection of influence coefficients and the phase of trial weight, these can help determine the final correction weight accurately in order to guarantee the balancing process prompt and efficient. Meanwhile, the restriction of practical location for adding weight and construction period of maintenance and production for the units should be considered during the high efficiency balancing in field. These strategies and techniques of high efficiency balancing have practical application value in promoting the technology of field balancing for turbo- generator units with large capacity.

The paper considers the actual task of construction engineering: the elimination of harmful (undesirable) vibration phenomena in composite metal building structures and bridge cable-stayed systems – complete damping and vibration of the bridge cable-stayed system entails the calming of the entire bridge structure. Similar problems arise when designing devices that lead to dynamic rest of network antenna structures. The presentation of the material is based on the main provisions of the modern theory of optimal control of distributed differential systems with network carriers. An extensive literature is devoted to the sanctification of the research of the mentioned problem, however, an exhaustive solution of the problems of controlling elastic structures by external influences of various types (distributed, boundary, starting, and point control) and a comparative analysis of their properties did not find their place in these studies. In addition, the ways of algorithmization of previously obtained results are not always specified. The paper uses a mathematical model describing small oscillations of a spatial string system. It is convenient to use such a system and approaches to the analysis of its mathematical model as a fairly simple fundamental object when considering a more complex object, which is an elastic system of more complex elements. The results of the work are presented in a form that makes it possible to formalize the computational procedure, which is convenient for algorithmizing the process of determining the solution, and therefore for the modern application of computer technology. It remains to add that the work indicates ways to transfer the obtained results to the case of analyzing optimal control problems with carriers on multidimensional network-like domains.

Unmanned aerial vehicle (UAV) has been widely used in power system inspection. Most of the existing UAVs are equipped with visible and infrared cameras for fast full spectrum photography and fault monitoring of transmission lines. When the transmission line has defects such as aging, broken strand and serious corrosion, the traditional image recognition, infrared and other observation methods can not identify these defects well. In order to realize the rapid detection and judgment of broken strand, this paper analyzes the characteristics of electromagnetic interference of transmission line, and constructs the electromagnetic interference measurement system based on patrol UAV. The research results of this paper can realize the real-time detection and alarm of broken strand, and provide technical support for line inspection.

In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.

As one of the core algorithms of machine vision, the mobile image multi-label recognition algorithm has received extensive attention from researchers in recent years and has been widely used in cutting-edge fields such as deep learning framework paddlepaddle platform, video surveillance, intelligent robots, and unmanned aerial vehicles. However, the existing recognition algorithms are not completely satisfied with the practical application in life and production. Due to the complexity of the platform environment, they can often only propose specific solutions based on existing problems, and there is no universal algorithm that is suitable for all kinds of Complex environment. The purpose of this paper is to study the multi-label recognition algorithm of moving images based on PaddlePaddle platform. This research mainly analyzes and researches the mobile image multi-tag space deployment plan and the multi-tag recognition algorithm, and further improves the tag reading rate and recognition reliability of the mobile image on the PaddlePaddle platform. This research first analyzes several key factors that affect the performance of UHF recognition system, considers the improvement plan of PaddlePaddle platform’s mobile image multi-tag recognition algorithm from the two aspects of space diversity and frequency diversity, and finally determines the multiple The label space diversity scheme, and the introduction of a multi-label optimization recognition algorithm to improve the recognition efficiency of the PaddlePaddle platform’s mobile image multi-label. Experimental data shows that the reading rate can reach 0.907 when identifying 300 tags in the experiment, and when the number of tags is greater than 300, the reading rate is close to 1, which verifies that the algorithm proposed in this paper is used in the multi-tag recognition of moving images on the PaddlePaddle platform.

The paper considers the problem of modeling the dependence of the value of thermal energy production by an electric power company on the air temperature using neural network technology. As an example of an electric power company producing thermal energy, the Public Joint-Stock Company (PJSC) Yakutskenergo. As consumers of thermal energy, organizations, enterprises and the population of the city of Yakutsk, are located at latitude 62 and characterized by a cold northern climate. The numerical experiments carried out in this paper have shown that the general trend of the temperature dependence of thermal energy production, observed empirically, is well described by a neural network

The paper discusses the problem of poor quality of graphical user interfaces used for various software products. The provided analysis shows that modern software has poor-quality interfaces, and there are no flexible programs for evaluating such interfaces. The paper highlights and describes the characteristics that are used to assess the quality of interfaces, such as operator speed, error rates, skill retention, and subjective satisfaction. A formalized approach to assess the speed of searching for information and functional objects is proposed. It is based on such characteristics as the time of fixation of the gaze, movement of the eye, the volume of a person’s operative memory, the zone of clear vision, the path of the user’s gaze information search. An algorithm for estimating the speed of searching for information and functional elements is proposed. This algorithm can be implemented and used for further design of a software application based on it. The algorithm makes it possible to automatically evaluate both a particular interface and to compare the interfaces of different programs.

As a revolutionary technology to realize the construction industry information, BIM technology has been vigorously developed in China in recent years. The whole life cycle of a construction project includes planning stage, design stage, construction stage, operation and maintenance stage, with many participating units. This paper mainly studies the architectural design of building information model technology under the Internet era. This paper analyzes and combs the traditional structural design process, and summarizes the problems and defects existing in the traditional structural design process. Combined with the characteristics of BIM technology application, the BIM technology is combined with the traditional structural design process. Taking Project A as an example, the causality analysis, the analytic hierarchy process and the fuzzy comprehensive evaluation method are used. The architectural design quality of Project a based on BIM technology is evaluated, and the effect of adopting BIM technology in the design of Project A is compared and analyzed.

The Inner Mongolia has abundant solar energy and electricity resources. Because of the long distance between cities, transmission lines are too long, making it difficult to check lines. In order to solve the problems existing in the inspection work, this paper studies a kind of outdoor inspection vehicle using solar energy, the energy system of the inspection vehicle can independently complete the charge and discharge, so as to realize the inspection task. This paper focuses on the energy autonomy of the on-site inspection vehicle for solar energy. According to the design requirements of the inspection vehicle, appropriate parts are selected to build an energy autonomy inspection system for the inspection vehicle. Then the solar tracking algorithm and maximum power tracking control algorithm are used to improve the conversion rate of solar panels and achieve fast charging. Finally, the hardware and software of the solar controller are designed, and the corresponding functions are debugged.

Since the concept of smart grid was defined, it has aroused the interest of many researchers, and many countries have also regarded smart grid as a key research project. Because it is the field of power grid research in the future, the structure of the smart grid becomes more complex with the deepening of research, the scale becomes larger, and the data contained in the system is bound to increase rapidly. Flexible DC grid technology has the characteristics of high reliability, good fault tolerance and good scalability, and can be used to solve some problems encountered in the application of smart grids. Based on the structure of the smart grid information system, this paper studies the characteristics of the smart grid data, as well as some safety issues encountered in the data, applies flexible DC power distribution technology, designs a new smart grid equipment management architecture, and System implementation was carried out. Through experimental verification, the new architecture proposed in this paper has high feasibility and safety. Finally, the realization of the management system function based on the new equipment management architecture and the analysis of the system function effect are given.

This paper discusses the research work done for controlling the humanoid robot manually using deep learning. For teaching, personal assistance, search and rescue humanoid robot are used. Controlling manually makes it to do any task without any explicitly programming. Existing technique for manually controlling the humanoid are heavily dependent on hardware and they are not cost efficient. This paper proposes a novel method for controlling the humanoid using a 2D camera. The image from the 2D camera is processed and skeleton of the human body is captured using deep learning. Then the skeleton is used to control the actuators present in the humanoid robot using image classifier and ROS. As a proof of concept the upper body of the humanoid robot is controlled in real time using this method.

The studies show that individual elements of a vessel component that are subjected to variable-repeated loads may be destroyed even at lower voltages than any other calculated component (unit). This problem is particularly important for the dynamically developing direction of unmanned navigation, which operation may be carried out with a reduced crew, in its absence – both remotely and autonomously. Conditions of cyclic variable loads in corrosive media also play an important role with a total number of operation cycles, which may reach many millions over the entire operation period. It should be noted that the statistics show that about 80% of accidents in maritime transport are caused by fatigue effects. This paper is devoted to the development of software that is able to predict fatigue failure of units and structures. The paper also descriunmanned vessel the application within an unmanned vessel, as well as the use of described mathematical algorithm in a device with the possibility of projection of augmented reality on vessel units for further prediction. The described method of predicting the strength and simplifying the service is relevant when remote security controls are required.

Currently, there are quite a large number of various scientific papers on the creation of a controlled asynchronous engine and the optimization of its modes, and the availability of acceptable results for practical implementation, but there is still no single generally accepted approach to solving the problem. In this regard, the issue of synthesis of scalar control systems that provide the minimum value of one or another criterion of energy efficiency is relevant. In this paper, we consider the obtained mathematical model of an asynchronous engine (AE), which differs from the known ones in that the parameters of the substitution scheme are expressed in terms of the stator and rotor conductivities. The energy characteristics of the AE in the sliding function are obtained, which make it possible to determine the dependences of the active and reactive components of the AE current and the possibility of their redistribution within the nominal value of the AE phase current. The principle of optimal frequency-current control of AE is formulated, which differs from the known ones in that, as an energy efficiency indicator, the value of the maximum energy efficiency of AE is used, expressed as the ratio of the active resistance of the rotor circuit to the total conductivity of the AE phase.

The MANET is an interesting research topic that has movable nodes. The mobile ad hoc system is an important research area. The nodes are connected by wireless connections. Thus it is vital to have a stable network because information and interaction are crucial in diverse fields such as security and catastrophe emergency response. Because of the decentralized system’s vibrant existence, these channels are vulnerable to various threats, including BHA, GHA, SHA. One of the well-known security issues in MANET is the black hole assault. Each node has a routing table containing information from the target node. The number of assaults in MANET is usually protocol networking assaults. The study focused on black hole attacks for discernment and decrease. A black hole attack node wrongly picks up all packet and collects them without passing them to an endpoint by taking a fresh path to the destination nodes. This paper presents BHA defence from assaults with the principle of Artificial Neural Network and calculates its efficiency according to the set of elements, such as efficiency, PDR and delay and energy utilization. This paper aims to devise an artificial neural network method for black hole discernment and reduction in MANET.

ML “machine learning” is an ever-expanding research field with plenty of possibilities for study and implementation. Mr. James Collin stated at MIT that ML is the technology defining this decade, even though it has had a meagre effect on healthcare. Several fresh businesses in the ML industry are applying themselves earnestly on healthcare. Even google has jumped in to the race and it has designed a ML application for identification of cancer tumour on mammograms. To identify skin cancer, Stanford uses a Deep Learning algorithm. Around one trillion GB of data is getting generated per year by the USA health system. Various academic experts and scientists have worked out various characteristics and several factors of risk involved in of chronic illness. Additional data stands for more learning for machine, but for higher precision, these many features need a huge quantity samples. So if machines can harvest clinically greater risk oriented feature it would definitely be better. Precision is improved when the data in the form of exploratory data analysis and feature engineering is pre-processed. The multi-class classification might be capable of evaluating a patient’s different disease risk levels. In health care, correct identification of the percentage of diseased individuals “sensitivity” is a primary concern rather than correct identification of the percentage of healthy individuals “ specificity”. Our paper introduces one of ML’s super challenger and emergent application i.e. Healthcare. The careful and sympathetic relation with care providers will always be necessary for the patients. ML cannot remove this, but will become instrumental for health professionals in improving and strengthening on-going care. Our paper discusses the several established models of ML along with its applications in healthcare system. We also bring up directions for imparting more efficiency to ML model. In addition to this we have also discussed ML’s case study for Brest cancer.

The research object is a pulsed bipolar power supply based on a bipolar pulse shaper of increased frequency for magnetron sputtering systems. The research subject is the electrophysical and electromagnetic processes occurring in the bipolar pulse shaper when it is operated with a magnetron sputtering system, as well as the control characteristics. The purpose of the paper is the possibility of creating a pulsed power supply with a power of up to several tens of kW, which makes it possible to increase the stability of the magnetron sputtering system. Besides, the outcomes of computer simulation of a power source based on a bipolar pulse shaper and control algorithm ensuring its stable and reliable operation in association with a magnetron sputtering system are reflected in the paper. The results show that the deviation of the output control characteristics of the bipolar pulse shaper from the analytically obtained characteristics does not exceed 3%. Circuit modeling is carried out in the Swicher CAD/LTspice software package. The mathematical SPICE models of the field-effect STY112N65M5 transistor, transistor IGBT of IRF4PF50WD and STTH8006 diode are taken from the websites of STMicroelectronics and International Rectifier manufacturers.

The paper studies the prospects of using telematics data to support effective solutions for tracking and monitoring the condition of the power supply system (PSS) components of unmanned vessels (UV) through the determination of positioning and, accordingly, operating time of PSS components. The paper attempts to describe one of the modules of the proposed integrated telematics system for UV monitoring, diagnostics and control. For practical implementation of telematics for unmanned merchant vessels, an algorithm has been developed to diagnose the technical condition of PSS components based on the operating time and condition of the units and components of the UV PSS. The results obtained by the algorithm show the technical condition of the observed technical equipment, time of its operation, and justify the need for repair or its complete replacement. The block diagram presents the algorithm that employs a minimal set of data to determine the use of technical equipment for further planning of its preventive maintenance. The paper provides the graphical interpretation of data on the time of PSS components operation and an example of displaying the interface of the remote operator of the UV. It is proposed to use telematics for tracking and monitoring the UV PSS operation in order to improve the quality of equipment maintenance, provide rational use of resources, extend its service life, and plan the repair time. In the future, it is proposed to create an integrated intelligent system based on telematics data, which allows the UV control and monitoring of the technical condition of its equipment.

Wells turbine is an important component in the oscillating water column (OWC) system. Thus, many researchers tend to improve the performance via experiment or computational fluid dynamics (CFD) simulation, which is cheaper. As the CFD method becomes more popular, the lack of evidence to support the parameters used during the CFD simulation becomes a big issue. This paper aims to review the CFD models applied to the Wells turbine for the OWC system. Journal papers from the past ten years were summarized in brief critique. As a summary, the FLUENT and CFX software are mostly used to simulate the Wells turbine flow problems while SST k-ω turbulence model is the widely used model. A grid independence test is essential when doing CFD simulation. In conclusion, this review paper can show the research gap for CFD simulation and can reduce the time in selecting suitable parameters when involving simulation in the Wells turbine.

Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fracture. The patient suffered from this disease will have poor quality of life. Simulation on the bone fracture risk would help medical doctors to make decision in their diagnosis. Detection of edges from the OI images is very important as it helps radiologist to segmentize cortical and cancellous bone to make a good 3D bone model for analysis. The purpose of this paper is to review the fundamentals of fuzzy logic in edge detection of OI bone as it is yet to be implemented. Several fuzzy logic concepts are reviewed by previous studies which include fuzziness, membership functions and fuzzy sets regarding digital images. The OI images were produced by modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, or Computed Tomography (CT). In summary, researchers from the reviewed papers concluded that fuzzy logic can be implemented to detect edges in noisy clinical images.

Data innovation worldview empowers pervasive access to shared pools of configurable framework assets and more significant level administrations that can be quickly provisioned with minimal administration exertion, regularly over the Internet. Cloud computing provides dynamic asset administrations for the client on request, and it is becoming increasingly popular. Cloud administrations are conveyed from server farms all through the world (as it is otherwise called Networks of systems). Outsider associations are additionally present there, because of which some significant security dangers came and make organize helplessly so the security of cloud information is imperative to keep over Cloud architecture to offer better support, ease administrations, and so forth. Despite that there are a few undermined hazards in cloud computing security. We will be concentrating majorly on cloud computing fraught with threats and how to overcome those threats. This paper is intended to introduce information about the most current attacks on cloud computing, just as safety efforts. The seriousness and impact of these attacks are talked about alongside genuine instances of these attacks. The paper likewise proposes alleviation strategies that can be utilized to decrease or dispose of the risks of the threats talked about. Moreover, the general cloud security proposals are given.

In the design of large-scale steel structure, the design calculation of the structure is usually based on the service state. But in the process of steel structure construction, the displacement and stress produced in the construction process cannot be completely consistent with that in the design state. With the progress of construction process, the deformation and stress caused by self-weight and construction load gradually accumulate until the structure is formed, which will produce construction error relative to the design state. In this paper, FEM method was used to simulate the construction process of the steel grid roof of the theater in the Beijing Sub Center Theater project, and the influence of the accumulated deformation and stress on the mechanical properties of the structure during the construction process was analyzed. The results show that the cumulative deformation and stress in the construction process have a great influence on the final deformation and stress distribution of the structure. The maximum vertical displacement in the actual construction state is 599.6% of that in the design state, and the maximum stress is 119.7% of that in the design state. This will lead to the difference between the construction-completed structure and the design structure, which makes the design result dangerous. The measures to optimize the construction process were also given in this paper, which provides guidance for the construction process of large-scale steel structure.