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Light-reflecting characteristics play a crucial role in optically rewritable liquid crystal display (electronic paper). By using a special optimizer software, light-reflecting characteristics of the twisted nematic cell were calculated. The achieved values of the normalized reflection coefficients range within ∼ 0.03 - 0.43, which is comparable or better than other commercial technologies. The calculated contrast ratio of the device is 10 : 1. A 2D image, which can be displayed on the electronic paper was obtained. Comparison of the simulated background color and contrast ratio is very similar with the results, which were obtained in experimental cells of other studies. Our calculations indicate that angular dependence of the reflectance coefficient is almost uniform within a wide range of viewing angles.

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

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

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

This paper is based of policy to diversify energy from renewable energy, one of the types of renewable energy is biodiesel. The purpose of this paper is to explain the water content in biodiesel with palm oil added, and the water content in biodiesel with coconut oil added, and compare the water content of the two types of biodiesel. The type of research used is the research and development method, testing the water content of the biodiesel at the Sucofindo Padang Laboratory and the Pertamina Padang Laboratory. The results showed that the water content of biodiesel with palm oil had a lower water content compared to biodiesel with coconut oil. Referring to the provisions of the Indonesian National Standard (SNI 7182:2015) that the water content in biodiesel is 0.05% as the maximum limit, while referring to Gaikindo (Association of Indonesian Automotive Industries), the maximum water content in biodiesel is 0.020% to 0.025%, then biodiesel B5, B10, B15, and B20, with palm oil added, and biodiesel B5 and B10 with coconut oil added as fuel can be used for diesel engines.

This study focus on the removal of textile dyeing effluent by analyzing the physio-chemical parameters such as color, turbidity, pH, Chemical Oxygen Demand (COD). Activated carbon is used for removing color from the wastewater collected from silkworm textile industry, Bishnupur, West Bengal. We have used activated carbon made with coconut shell which is ecofriendly and low in cost. The results of the analysis were compared with the water quality standards of BIS (Bureau of Indian Standard) for further application of the treated water in agricultural field. Also, this paper included the case study on textile dyeing industry in Bankura district, West Bengal, India and its impacts of pollution load in the environment.

Biomass-based energy conversion and storage applications have proven to be the most effective technology for practical and sustainable applications. However, their further development was hindered by poor electrode performance. Naturally, abundant biomass is a green alternative carbon source with many desirable properties. This study presents a relatively easy approach for the synthesis of activated carbon-based electrode materials derived from natural biomass with an emphasis on supercapacitor applications. The selected biomass waste is Longan leaves. The precursor was converted into activated carbon through ZnCl2 impregnation at three different concentrations in high-temperature pyrolysis. All activated carbon confirmed a good amorphous structure. Furthermore, oxidative compounds were also found to have an effect on their electrochemical properties. supercapacitor cells prepared in a two-electrode system exhibit high electro-capacitive properties with a specific capacitance of 169.83 F g−1 at a constant current density of 1.0 A g−1 in an aqueous electrolyte of 1 M H2SO4. Furthermore, the optimum energy density was found in LF0.5 samples as high as 19.04 Wh kg−1 at a maximum power density of 124.37 W kg−1.

This study aims to assess the overall performance of a membrane bioreactor and reverse osmosis membrane for the treatment of textile effluent as well as to investigate their ability in meeting the water reuse standards. The laboratory-scale MBR-RO units were fed with textile wastewater, and the MBR effluent performance was evaluated through water quality for the parameters like total suspended solids (TSS), chemical oxygen demand (COD), and colour. The results reveal that the MBR system yields good quality permeate water. We obtained 99%, 90.3%, and 82.5% (average) reduction of TSS, COD, and colour, respectively, with this system. Moreover, the MBR technology is highly effective for pre-RO treatment. We also studied the impact of pressure and temperature applied to the RO membrane on the efficiency for eliminating TDS, COD, colour, and the permeate flux. The results demonstrated that the applied pressure positively affected while the feed temperature negatively affects the overall removal efficiency. This study confirms that the feed temperature, besides pressure, have a positive effect on the flux for an RO system.

Textile is one of the important industry. Conversion of fiber to fabric takes place which involves many process and synthetic chemicals. The fabric is then dyed or printed, fabricated into clothes. The extraction of dyes from the wastewater industry becomes a worldwide issue. Water contamination due to industrial discharge is the big threat to the environment. Hence an attempt was made in this study to treat textile wastewater using natural anionic polysaccharide sodium alginate as adsorbing agent. The optimum condition was obtained by varying dosage, pH, flow rate and depth of beads for varying contact time. For optimum conditions, the textile wastewater was analyzed. Results showed 32%, 56% and 75% removal of sulphates, alkalinity and nitrates respectively. Hence sodium alginate proved efficient in treatment of textile wastewater.

This study attempts to apply the concept of sustainability by reducing the environmental pollution of plastic waste, especially Polyethylene Terephthalate (PET) that are used in production soft drinks bottles. The plastic bottles were collected, roughened) Roughen the surface by using a metal clip after exposing it to heat, and then place it on smooth surfaces) and shredded into fibres of specific size and shape with an aspect ratio (L/D) of 34.5. Various proportions of PET (i.e. 0.5, 1.0, and 1.5%) have been used in the production of ordinary concrete. The influence of PET fibers on the properties of concrete was studied, such as workability, compressive and splitting tensile strengths. Furthermore, the shear characteristics of continuous reinforced concrete deep beams with dimensions of (150×300×2000) mm produced from such concrete were also investigated. For all percentage of replacements, The fresh property, compressive strength, and tensile strength of PET fibre encapsulated cement concrete were investigated. With the addition of plastic fibre, compressive strength increased marginally, but tensile strength measurements showed a substantial improvement over the control specimen. it has been found that there was an apparent impact of waste plastic fibers on the structural behavior of the investigated beams in terms of its brittleness. The increase in the ultimate load capacity With the increase in the percentage of fiber until a percentage(1%), then it begins to decrease after this percentage of continuous deep beams refers to the use of waste plastic fibers to improve the tensile properties of concrete and to limit the spread and width of cracks within the structure.

To treat domestic sewage with high efficacy and low cost, the technology of shortcut simultaneous nitrogen removal was used with a new biofilm reactor. The influences on nitrogen removal effect by start-up procedure, nitrogen transformation characteristics and main factors (hydraulic retention time, dissolved oxygen, carbon-nitrogen ratio) were analyzed. The results indicate that: The reactor finished start-up of shortcut simultaneous nitrogen removal in 14 days of cultivation time after the biofilm cultured in the carrier. The ammonia nitrogen removal rate, simultaneous nitrification-denitrification rate and cumulative nitrite nitrogen rate respectively reached 91.3%, 82.7% and 63.4% in the last phase of start-up procedure. To remove ammonia nitrogen with the technology of shortcut simultaneous nitrogen removal efficiently and steadily, hydraulic retention time, dissolved oxygen, carbon-nitrogen ratio should be controlled in 3.56 hours, 1.5 mg/L and 5~8.

Wastewater of textile industry containing any pollutants, turbid and strong color appearance. Electrocoagulation (EC) is a wastewater treatment alternative, which are some factors: distance of electrode, voltage application, and time operation. This research was determined the optimum voltage density and flow rate by continuous system EC. The couples of Al electrode was immercing in the EC chamber by various distance: 1, 5; 3.0; 4.5; 6 cm, and applide 18 volts. The wastewater was passed to the EC chamber by ascending in varieous flow rate: 1, 0; 1.5; 2.0; 2.5 mL s−1. The product was evaluated according to the regulations PermenLH RI no 5/2014, including: COD, TSS, pH, DO, Conductivity, and Al concentration. The result optimum voltage density application is 12 Vcm−1 (18 volts/1.5 cm), and a flow rate 1 mL s−1. COD Removel is 91.80% (1.683 to 138 mgL−1; fulfil the regulations), TSS removel is 87.83% (937 to 114 mg L−1, (not fullfil the regulations), color degradation is 83.98% (239, 14 to 38, 31 mgL−1 PtCo). The others are pH increase in the range of tolerant, DO increase up to 8, 2 mg L−1, Conductivity increase up to 5350 μs cm−1, and Al increase up to 2, 71 mgL−1.

Plasma Dielectric Barrier Discharge (DBD) can be applied to degrade textile wastewater. In this study, a DBD plasma reactor was composed of two electrodes of stainless-steel mesh as the outer electrode and Cu wire as the inner electrode with pyrex glass as the dielectric barrier. The electrodes were connected to an AC voltage of 3.07 kV. As much as 1500 ml of textile wastewater has flowed continuously into the system. Variations time of treatment were 0, 30, 60, 90, and 120 minutes. Physical parameters of textile wastewater such as pH, total dissolved solid (TDS), and absorbance were measured before and after treatment to determine the effect of plasma. As a result, the highest degradation efficiency of 54.29% was obtained after treating the textile wastewater for 120 min.

The paper is devoted to experimental study of heat exchange and pressure drop of channel with various geometry twisted bands. The studies were carried out in the range of operating modes parameters of the nuclear power units’ heat exchange equipment. The three different designs of intensifiers are presented in the paper. The values of heat transfer coefficient and pressure drop are obtained. The dependences of the Nusselt number (Nu) on the Reynolds number (Re) were calculated. The comparative analysis of intensifiers is made. The efficiency factor was also calculated on experimental data. The most optimal geometry form of intensifier was selected.

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.

The use of georeferenced information system and Light Detection and Ranging (LiDAR) data in combination with traditional data analytics tools is very promising in urban scale engineering and especially in energy urban planning. This paper explores the use of new DH networks for industrial waste heat exploitation and for that purpose, a case-study in Vitoria-Gasteiz (Spain) is proposed. The methodology explained in this paper explores the incorporation of data from industrial emplacements, buildings and road network in order to identify optimal areas in the city for the construction of a new district-heating network. An area of influence of a buffer of radius 1.5km from the industry location is defined and the proposed algorithm divides this area into grids of different sizes. The path for the network is calculated by optimizing the economic performance of the network. The results show that the district-heating may be built in the south-west direction from the industry and among the 40 configurations studied, payback periods from 6 to 8.5 years are obtained.

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

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

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

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

This paper investigates the potential sources of renewable energy, in particular biofuels. Biofuels tend to contain multiple harmful impurities that need to be separated if the biofuel is to have good energy performance, and the systems that run on it to produce electricity or heat are to operate more reliably. The paper discusses use of hydrocyclones as the most productive and reliable biofuel purification method. It dwells upon the factors that negatively affect separation of mixtures in a hydrocyclone, which are attributable to the complex hydrodynamics of the flow in such a unit. In order to eliminate these factors, the authors hereof have developed two hydrocyclone designs. An experimental test bench was designed and made to test these designs. Parts of the units were 3D printed from an environmentally friendly material. For testing, we used a biodiesel made from waste cooking oil with an impurity content of 23%. Experiments showed a maximum separation rate of 94.2%. The proposed solutions did improve the effectiveness of biodiesel mixture separation. These designs can be effectively used to separate non-homogeneous mixtures.

Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

In order to further improve accuracy and stability of detection of combustion coal fallout propensity of cigarettes, author of the paper adopted computational fluid dynamics (CFD) technology for a three-dimensional numerical simulation of exhaust system of detection instrument, aiming to study characteristics of flow field near cigarettes. Moreover, a simulation model of eight-channel exhaust enclosure was established, obtaining vector diagram for flow velocity of flow field, velocity contour diagram, and pressure distribution cloud diagram. According to findings, flow field of eight channels is evenly distributed, with slow flow velocity around the instrument but furious inside channels. The wind velocity of cigarette monitoring channel is stable at about 200mm/s specified as per standard. However, there is significant change in pressure and flow velocity at the corners of channels, causing local turbulence. In experiments, average wind velocity of 8 monitoring channels was measured, and simulation results were compared with experiment data. Eventually, a conclusion is drawn that simulation result at cigarette monitoring channels changes consistently with the experimental data, with small errors as a whole. Therefore, the designed exhaust system complies with regulations on wind velocity stipulated by YC/T558-2018 Cigarettes—Determination of Combustion Coal Fallout Propensity of Burning Cigarettes. In a word, this paper is hoped to provide technical support for analogue simulation of exhaust system of cigarette detection instrument, and improve detection accuracy.

Cladding tube is one of the key components in nuclear power reactor. In this paper, the forming process of a new cladding tube is investigated, namely ribbed tube. The influence of semi die angle on the forming process of ribbed tube is studied. Firstly, the strain-stress curves of 316L stainless steel (SS) at different strain rates are obtained. Based on the strain-stress curves, a modified Johnson-Cook constitutive model which ignore the influence of temperature are used, and the parameters of the modified model are determined. Then, the modified Johnson-Cook is used to establish the finite element (FE) model of ribbed tube drawing process, and two pass drawing process is adopted in this paper. Finally, the influence of semi die angle on the forming process (forming quality and drawing force) of ribbed tube is elucidated, and the credibility of the simulation is confirmed by comparing the simulation and experimental results. The results show that when the semi die angle less than 8 °, all the formed ribbed tubes meet the requirement, but there are inner grooves appear at the inner wall, and the inner grooves cannot be avoided in the drawing process. Moreover, when the semi die angle is 8 °, the inner grooves have smallest depth.

The supercritical carbon dioxide (SCO2) Brayton cycle takes advantage of the special physical properties of carbon dioxide near the critical point (31.1 °C, 7.39MPa), and has higher energy conversion efficiency than the current large-scale steam power cycle. This cycle can be widely used in the field of power generation, but a lot of research work is still needed in terms of component parameters and layout under different working conditions. In this regard, the purpose of this paper is to study the influence of supercritical carbon dioxide Brayton cycle parameters on cycle efficiency and its optimization strategy. Based on the first law of thermodynamics, this paper uses Aspen Plus software to establish S-CO2 Brayton cycle system models with different circulation arrangements. In this paper, the existing algorithm of the simulation system and the newly-built algorithm are used to build the S-CO2 shunt and recompression Brayton cycle system model, and the accuracy of the model is verified with experimental data from literature. Then this paper conducts disturbance experiments on the model to study the influence of heater heating, valve opening and precooler cooling on the system, and analyze the dynamic characteristics of the system. Experimental results show that the thermal efficiency of the simple Brayton cycle is much lower than that of the recompression Brayton cycle and the split recompression Brayton cycle under higher parameters. The compressor outlet pressure and the turbine inlet temperature have an effect on the efficiency of the recompression Brayton cycle. The impact is significant, and the optimal value of the compressor shunt coefficient is between 0.5-0.7, which provides a reference for the layout optimization method of the SCO2 Brayton cycle and the optimization of the same type of power generation cycle.

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%.

The acceleration of urbanization has brought about rapid economic development, but at the same time, it has also brought some damage to the ecological environment. The proportion of hardened area of the ground is higher and higher, and the rainwater runoff pollution caused by rainfall is more and more serious. In order to follow the sustainable development strategy, and for the more stable and high-speed economic development, the control of rainwater runoff pollution is urgent. The purpose of this paper is to simulate the urban storm water runoff control and find the most suitable scheme for storm water runoff pollution control. Because the simulation of SWMM is more accurate than other models, it can directly reflect the situation of rainwater runoff pollution, so the model selected for rainwater runoff in this paper is SWMM, and then build the model, through the collection and collation of the basic data of the study area, the generalization of the sub catchment area and drainage network is completed. Through the analysis of the characteristics of the study area, the rainwater garden and permeable pavement are determined as the scheme to control the rainwater runoff in the study area. Finally, the SWMM model is used to simulate the control effect of rainwater garden and pervious pavement on rainwater runoff pollution control. The experimental results show that the storm water garden can effectively control the impact of SS scouring effect on the environment, significantly reduce the discharge of SS, and significantly reduce the peak concentration of SS, and its ability to control SS increases with the thickness of the surface plant layer. The control ability of rain permeable brick pavement to SS increases with the increase of surface porosity, that is, the control effect of SS is the best when the porosity is 20%.

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.

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.

Based on the demand for smart grid 5G network, this paper studies 5G mobile communication technology and the industry's 5G private network construction mode. Combined with power grid requirements, this paper focuses on analyzing the uninterrupted 5G private network solution, the principal of the solution is also explained. Taking a 5G test network deployed at a converter station as an example, the construction, isolation, operation, and maintenance plans of the uninterrupted 5G private network solution in the power communication network is proposed, the actual tests verify the feasibility of the continuous 5G private network. This paper will provide reference for the promotion and application of 5G private network technology in the power grid.

Aiming at the series fault arc phenomenon in landscape lighting and the hidden dangers of electrical fires, in this paper, a landscape power supply series fault arc model is constructed and its model is simulated. The simulation results show that when a fault occurs, the arc current becomes smaller (almost zero) due to the increase in the impedance of the lighting circuit; this phenomenon is called the “current zero off” phenomenon of the fault arc current. The current zero off phenomenon of the fault arc current is the main fault feature in the landscape lighting circuit. In this paper, the wavelet algorithm is used to detect the fault current waveform. According to the fault characteristics, by judging whether the modulus maximum value of the wavelet coefficient has periodic characteristics with an interval of 100±15 sampling points, it is analyzed whether a series-type arc fault occurs. The built physical model verifies the feasibility and correctness of the arc detection algorithm. The research results of this paper have certain reference value for the detection and application of fault arc.

The unusable fuel quantity plays a key basis for calibration of aircraft fuel gauge and fuel measurement system. Using large helicopter fuel tanks as case study, the paper described airworthiness requirements for fuel system design and current research development. In particular, the paper presented an analysis method can be utilized and referenced as means of compliance for airworthiness requirements. This method will reduce the qualification time and save money for physical and dynamic bench test/flight test in the future.

Infrared thermography technology is widely used in the thermal condition detection of insulators due to its advantages of non-contact, sensitive, online detection. To realize the automatic detection of the operating condition of insulators in complex environments, this paper proposes a method for the recognition and location of the insulator based on Region-based Fully Convolutional Networks (R-FCN). The model was trained and tested on the constructed insulator infrared data set, compared with the SSD model. The results showed that the R-FCN detecting insulators can not only accurately locate insulators, but have an AP (average precision) value as high as 89.2%. Therefore, the findings in this paper have verified that R-FCN has great advantages in the recognition and location of infrared images of insulators and has practical application value.

With advances in the mobile communications, many service-related tasks can be made quickly and easily. Mobile banking is one such service that has eliminated the need for a consumer to go to a branch to carry out many common transactions. In a country like India, where the last mile reach through brick-and-mortar banking facilities, mobile phones can complement the reach. This paper describes how mobile cloud architecture can be employed for banking and services to customers to enhance their banking experience as well as ensuring information security. This paper focuses on cloud-based risk architecture for banking solutions to address various issues related to mobile banking such as processing speed and storage capacity. Improved random forecast algorithm is used for the evaluation of the system. This proposed system achieves 99% of the system.

The implementation of training complexes based on virtual reality for the training of specialists in machine-building, chemical, mining and other industries is associated with the need to solve a number of tasks. These include simulating various physical processes (temperature, humidity, and air pressure), loads (speed of movement and angle of inclination of the surface), and other external factors that cannot be reliably simulated in virtual reality. Therefore, an urgent task is to integrate various simulation systems into the software and hardware platform of virtual training complexes, which will provide a realistic immersion in the subject area due to virtual reality, as well as simulate the necessary external influences on the student. The paper considers this process by the example of integration of a simulation system of physical loads into virtual training complexes. The basis of the simulation system under consideration is a controlled treadmill. The use of treadmills allows students to develop muscle memory, perform physical training and increase the degree of immersion in virtual reality, which positively affects the effectiveness of their training. However, their integration requires the solution of a number of practical tasks for the transfer of information between the individual subsystems of the virtual training complex. The paper considers algorithms and software for solving these problems. The described approaches can be used to integrate various simulation systems into the software and hardware platform of virtual training complexes for the organization of comprehensive training of specialists.

This paper takes the physical model of Radar Cat’s Eye as the research object, based on geomagicdesign software’s powerful reverse modeling function, the reverse modeling and design process of Radar Cat’s Eye is explained in detail. The detailed steps and main precautions of scanner calibration in the process of Radar Cat’s Eye reverse modeling are given. At the same time, the main contents of model scanning, data processing, reverse modeling, model comparative analysis and so on are also introduced and explained in detail. In short, the relevant contents of this paper provide reference and help for the reverse modeling and design of other models.

This paper discusses structuring of hardware and software for an autonomous social robot. It shows that the real-world social robot operations face the challenge of environmental variability and uncertainty of the objective parameters. Thus, a social robot must be capable of situation analysis for better autonomy. We propose a modular distributed structure of the control system. Separate modules monitor the status and control the subsystems of the robot. General coordination of subsystems is provided by the Supervisor module. For the robot to function autonomously, the Supervisor must be capable of situation analysis and its key functions: objective retrieval and analysis, situation description, configuring and strategizing the solution. The robot’s sensory inputs help acquire the objective and its parameters to describe the situation. Description relies on the database of a priori knowledge of the environment and its objects. Analysis is linked to a reduction in the uncertainty of the objective parameters and situation description. For a case study, the paper demonstrates a maze-solving strategy as affected by the situation.

The development of music is a tortuous process, and the network relationship between each genre and each artist is intricate. In order to have a better understanding of the history of music, this paper tells the stories hidden in the history of music by means of data processing. Firstly, this paper establishes a model to evaluate the similarity between music by using ISOMAP algorithm. At the same time, the forest evolution model was established to mark the most revolutionary musical characters. Finally, using the Page-Rank algorithm, we get the founders of several music genres. It turns out that the figures who led the development of music don’t coincide with the figures who revolutionized music. Through the analysis of this paper, we can more clearly understand the development of music and the evolution of genres.

A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

This paper presents an adaptive frequency Phase locked loop (PLL) based approach for grid connected multifunctional inverter is presented for residential applications. The proposed control structure deals with non-linear loads and also non ideal grid voltages like magnitude variation, frequency variation, unbalance harmonics, etc. and can effectively address the DC offset in a voltage signal which in turn improves the power quality. A third order generalized integrator based PLL with adaptive frequency and DC offset elimination blocks will effectively deal with the power grid voltage fluctuations, frequency variations and eliminate the phase difference between PLL output. This approach is implemented as control algorithm for single-stage single-phase grid connected multifunctional inverter topology for PV applications which feeds energy to the grid. The Maximum Power Point Tracking is obtained by Perturb & Observe method for extracting maximum power from a Photovoltaic system. A detailed analysis with the proposed control technique is presented in this paper. Experimental tests are conducted at various operating conditions to describe and verify the performance of the proposed control using MATLAB / Simulink.

The traditional hepaticcell carcinoma (HCC) pathological grading depends on biopsy, which will cause damage to the patient's body and is not suitable for everyone's pathological grading diagnosis. The purpose of this paper is to study the pathological grading of liver tumors on MRI images by using deep learning algorithm, so as to further improve the accuracy of HCC pathological grading. An improved network model based on SE-DenseNet is proposed. The nonlinear mapping relationship between feature channels is modeled and recalibrated using attention mechanism, and rich deep-seated features are extracted, so as to improve the feature expression ability of the network. The method proposed in this paper is verified on the data set including 197 patients, including 130 training sets and 67 test sets. The experimental results are evaluated by receiver operating characteristic (ROC) and area under the ROC curve (AUC). The improved SE-Densenet network achieves good results, and AUC 0.802 is obtained on the test set. The experimental results show that the method proposed in this paper can well predict the pathological grade of HCC.

with the rise and rapid development of mobile communication, intelligent terminal and data system, we are entering the era of mobile Internet. In recent years, more and more data need to be processed and transmitted in daily life, and structured data is becoming more and more important. Among them, multi-mode matching technology can search data in a wider range. Matching for multiple patterns at a time avoids unnecessary matching, accelerates the matching process, and helps to find longer matching information and obtain higher accuracy. This paper mainly introduces the high camouflage intrusion detection method of structured database based on multi-mode matching. This paper uses the high disguised intrusion detection method of structured database based on multi-mode matching, collects sensitive information of wireless access points and stations through the communication of WLAN in multimodal matching, then intercepts and forges data packets to initiate replay attack. Replay attack is characterized by abnormal traffic in the network, which can be detected by statistical analysis. The experimental results show that the high camouflage intrusion detection method based on multi-mode matching makes the camouflage intrusion detection rate increase by 23%. The limitations of the design and research of camouflage intrusion detection are analyzed, discussed and summarized, so as to enrich the academic research results.

In this paper, a MATLAB Simulink model of a Quadruped Robot is presented alongside its remote, control and monitor user interface that has been developed by using the fundamentals of Internet of Things on a Node-Red Flow and the FRED-Cloud Server. Robotics and Automation over the recent years have developed exponentially and hence have been a key factor in the rise of Industry 4.0 which has usurped manual supervision and operation in industrial and manufacturing processes around the globe. The design and creation of technologically advanced robots integrated with computer-based software for their automation has not only successfully made the tasks facile to manage within short spans of time, but also has increased the efficiency notably. The stability and mobility of quadruped robots is considered to be ideal on differing terrains with minimal subtle changes, thereby making it an asset. Internet of Things on the other hand, has paved its way over the control of robots as well, with its unparalleled benefits. This paper is focused on the design and execution of the Quadiuped model which includes the observation of the various significant graphs achieved post simulation with respect to electrical values such as power and current consumption, and a visual animation of the robot running in the workspace. Furthermore, a single platform is developed and displayed that allows a user to log in for security puiposes and thereby, operate and monitor the functions and conditions of the bot easily, ranging from remote visual support, directional integrity, damage control and more, without the need of multiple platforms to carry out varying tasks with respect to control.

With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..

With the rapid development of informatization, computer database software systems have entered various fields of society, which has brought about the explosive growth of industry data. Faced with massive amounts of data, computers with limited storage capacity have to abandon some outdated data, and the application of various data mining technologies related to it has gradually matured. The purpose of this article is to discuss the application research of data mining technology in software engineering. This article analyzes the correlation analysis of a large number of bug repair source code update data and bug defect reports in the version control system SVN and the defect tracking system Bugzilla in the software engineering project development process, and tries to classify the bug report by data mining technology: defect changes and potential defects change. Starting from large-scale software engineering projects, apply data mining technology to the huge software engineeri ng knowledge base. Especially the software development and maintenance are explained, as well as the more challenging problems in the future. This paper uses data mining technology to study the dependency of the source code files of each module of the software system, and helps software developers quickly understand the software architecture by understanding the interrelationships between the modules, and provides suggestions for modification paths. Experimental research shows that this paper compares with F-measure and concludes that FL-M-GSpan algorithm is better than TS-M-GSpan algorithm. At the same time, it is found that the FL-M-GSpan algorithm always has a better accuracy rate close to 95%, while the TS-M-GSpan algorithm always has a better recall rate.

With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.

The power consumption readings of sub meter and total meter of distribution transformer of low-voltage users follow the law of conservation of energy. The meter power loss rate of abnormal low-voltage users must also be abnormal. This paper studies the solution of the meter power loss rate under the four abnormal power consumption scenarios of single (multi) user and full (partial) period. The traditional linear solution method has accurate identification effect for the abnormal power consumption scenario of full period, but it cannot identify the abnormal power consumption scenario of partial period. In this paper, an improved artificial fish swarm algorithm is proposed. By adjusting the fixed step to the adaptive step, the power loss rate of each sub meter is obtained, and the abnormal power users are pinpointed. The research results are verified by simulation examples on IEEE European Low Voltage Test Feeder. The results show that the improved artificial fish swarm algorithm in this paper can identify abnormal power users for the above four abnormal electric field scenarios. The algorithm provides a new alternative for the identification of abnormal low voltage users.

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.