Search

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In order to build a smart campus teaching system with good teaching efficiency, this paper improves the traditional ZigBee algorithm and builds a smart campus teaching system based on ZigBee wireless sensors. Moreover, this paper improves the estimation algorithm of students' specific location and direction on campus, and analyzes the network structure of the smart teaching system based on the actual needs of smart teaching, and builds a three-tier teaching structure system. In addition, on the basis of demand analysis, this paper sets the system function modules, analyzes the data processing effect of the network nodes of this smart campus through experimental research, and evaluates the system teaching effect. Finally, this paper uses simulation technology to analyze 100 sets of data to evaluate the data processing effect of ZigBee network. Through the experimental research results, we can see that the smart campus teaching system constructed in this paper has good results.

With the development of science and technology, the power source of the automobile industry has gradually changed from non-renewable energy sources such as petroleum to electric energy, a clean and renewable energy source. By the end of 2020, scientists have found that the global demand for batteries has greatly increased. The demand for power batteries has reached 50% of all battery demand, occupying half of the market. In addition, scientists also boldly predict that by 2025, China's consumption of batteries in the automobile industry will reach more than 100,000 tons per year. Faced with so much battery consumption, how to dispose of and recycle LiFePO4 used batteries has become the key to restricting the development of the industry. Failure to dispose of used batteries will not only hinder the development of the industry, but also cause serious pollution to the environment. The application of sensors today has been extended to various fields, and the emergence of satellite navigation and positioning technology is one of the driving forces to promote its development. However, the sensor cannot be directly applied to various environments. When in a complex environment, the obstruction of various objects in the environment will seriously affect the stability of the sensor. In order to ensure the normal operation of the sensor, positioning technology began to be applied to the sensor. The industrial production process of solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials discussed in this article is based on this perspective, through the use of computer vision positioning to reduce the impact of environmental factors on the accuracy of the sensor, thereby improving the quality of the battery. In order to match the image point of the waste lithium iron phosphate material with the space point, this paper creatively uses the image point and the point in the corresponding space field. In addition, the author also studied the camera's rear rendezvous method and proposed a new rear rendezvous method. In addition, in order to strengthen the detection of waste lithium iron phosphate materials, this paper strengthened the construction of the sensor hardware system and software system, and completed the upgrade of the waste lithium iron phosphate material detection system.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

The Internet and the Internet of Things (IoT) connect the world inside and outside the network through online and offline communication, which forms the original single network virtual platform. The integration of IoT, virtual reality and artificial intelligence can provide intelligent WebVR interaction for various application fields. However, we need to solve the problems of data lightweight, real-time data transmission based on the IoT, and visualization of scene data on the Web. This paper uses the IoT and virtual reality technology to propose a virtual reality scene modeling method based on the IoT platform, as well as the database dynamic loading management method and device of the virtual reality system, which includes building the database, storing the involved data structures for various scenes and functions in the virtual reality system. The time–space IoT engine is connected with the integrated service sharing center. The IoT platform collects data through the IoT sensor system and provides support for the application platform through the integrated service sharing center. The proposed method can meet the needs of large-scale data acquisition and modeling, shorten the loading time, improve the efficiency, and save the wasted computing resources and memory.

The 5G networks promise to use advanced access techniques and modulation formats to achieve new requirements of the huge number of users such as higher data, high speed, and low latency. Millimeter-wave (MMW) signal and new modulation schemes such as universal filtered multicarrier (UFMC) and filtered bank multicarrier (FBMC) are playing a fundamental role to accomplish these requirements. UFMC and FBMC are classified under multicarrier modulation formats which are considered as a suitable modulation scheme in 5G and replacing a well-known modulation format of filtered orthogonal frequency division multiplexing (OFDM) which was used in 4G. This interest in using UFMC and FBMC in 5G wireless systems is the reduction of out-of-band (OOB) spectrum in UFMC and reduction of sidelobe suppression in FBMC. The contribution of this paper is to propose a model of wavelength conversion using a semiconductor optical amplifier (SOA) for both schemes UFMC and FBMC- off-set quadrature amplitude (OQAM). In addition, we analyze and compare their performance in terms of bit error rate (BER) and error vector magnitude (EVM) for a wavelength conversion application. Finally, we investigate the main disadvantage of OFDM which is the high Peak to Average Power Ratio (PAPR) for both schemes, and compare between them. Photonic switching of a 4 Gbps 16-QAM UFMC and FBMC-OQAM signal centered at 50 GHz is performed. The optical single-sideband (OSSB) signal is generated with 18.85 dB of sideband suppression ratio (SSR). The results show that the best values of BER and EVM can be obtained when the injection current (IC) is 0.7–0.9A. For converted signal using FBMC-OQAM, it has power penalty ∼ 2.5 dB at BER threshold 1 × 10 -3 and has also ∼ 2 dB power penalty compared to the UFMC scheme at EVM threshold 5.6%. In addition, the wavelength switching system with FBMC and UFMC modulation schemes achieves the same PAPR.

A novel biomass-driven heat and power cogeneration system comprising biomass gasification, a gas turbine, a Stirling engine, and a supercritical carbon dioxide cycle integrated with a domestic water heater was proposed in this work. Different biomass feedstocks (paper, wood, paddy husk, and municipal solid waste) were used in the gasifier as the input fuel. The devised system was analyzed from energy, exergy, exergoeconomic, and environmental viewpoints. Moreover, the effect of integrating the Stirling engine with the stand-alone CHP system is studied. Moreover, a detailed parametric analysis was performed to assess the effect of varying operating parameters on system efficiency. Finally, multi-objective optimization using genetic algorithm in MATLAB software was performed to obtain the optimum operating points. According to the results, using municipal solid waste as the input biomass resulted in the highest exergy efficiency by 41.36 % and the lowest C O 2 emission by 0.9021 t / M W h . Also, the system with the Stirling engine had a higher exergy efficiency and lower C O 2 emission than the system without the Stirling engine. According to the optimization results, the maximum obtainable exergy efficiency was 42.03 % , which was related to MSW. Also, the minimum achievable c p , t o t was 10.94 $ / G J , attributable to the respective paddy husk.

This paper explores the data carrying capacity enhancement in free space optics (FSO) transmission systems by employing orbital angular momentum (OAM) multiplexing technique. Specifically, simulative investigation of OAM-FSO terrestrial transmission is carried out under varying weather conditions. A net data capacity of 40 Gb/s over free space transmission medium is demonstrated using four distinct OAM beams (LG0,0, LG0,13, LG0,40, and LG0,80). The performance is compared for alternate mark inversion (AMI), return-to-zero (RZ), and non-return-to-zero (NRZ) encoding schemes. The results showed that the best performance occurred when using NRZ modulation and a favorable 40 Gb/s transmission along a range varying from 64 to 800 m depending on the external weather conditions is reported.

The poor acoustic environment of the waiting hall of the high-speed railway station will result in poor passenger waiting process experience, and there is currently no research related to reducing the poor acoustic environment of the waiting hall. Based on this, this study studied the factors affecting the acoustic environment of the high-speed rail waiting hall from various aspects and used the image simulation technology to carry out the simulation analysis of the actual situation and focused on the analysis of the sound absorption materials in the waiting hall. At the same time, this study obtained the actual noise situation of the high-speed railway station through data collection and compared and analyzed the passenger experience of the high-speed rail hall under the influence of different factors. In addition to this, the reverberation time of the hall was controlled by establishing a corresponding model for analysis and selection and arrangement of materials. Then, the reverberation time was calculated, and the software is used to simulate the test. Therefore, before the project was completed, the reverberation time was theoretically controlled to a more appropriate range. The research shows that the method provided in this paper has certain effects and can provide theoretical reference for subsequent related research.

This paper proposes a simple and practical method for retrofitting deficient (non-seismic detailed) exterior beam-column joints based on fixing steel plates and angles around the joint with different configurations. Six half-scale exterior RC beam-column joints were tested under quasi-static cyclic loading with a constant axial load on the column. The experimental program included two control specimens representing code-detailed and deficient joints in addition to four retrofitted specimens. The proposed retrofit methods were designed to compensate the lack of joint transverse reinforcement using different steel arrangements (X-shape, horizontal plates) and the short development length of beam bars using angles tied by external or internal anchors. One of the retrofit methods is applicable in the presence of a transverse beam. The test results showed that the deficient joint exhibited brittle joint shear failure, unlike the code-detailed joint which failed by flexure in the beam. The proposed retrofitting methods effectively prevented the brittle joint shear failure and increased the joint strength, ductility, stiffness, and energy dissipation. A softened strut and tie model was applied and modified to predict the joint shear strength of control and retrofitted joints and it showed a good agreement when compared with the experimental results.

For smart cities, the successful large-scale implementation of solar PV technology, Quality Certification and Standards are mandatory. The International Electrotechnical Commission (IEC) is a global organization for standardization consisting of all IEC national committees. The IEC PV standards comprise IEC technical committee 82 solar PV Energy System (IEC TC82) which develops and adopts all Photovoltaic related standards. There are nearly 80 standards applicable to photovoltaic and five working groups in IEC TC82. For necessary safety requirements ‘Quality and Standards’ technologically need to be revised and up to date. This paper presents PV standards developed by various technical committees worldwide, mainly focusing on various IEC PV standards, gaps identified by them and the recommendations provided by the committee in recent times. The breakup costs of the various sub-systems of a PV installation with an illustrative example for India is also discussed. It is intended to provide a guideline for consumers and investors, opting for solar PV installations.

Underwater imaging is a crucial and challenging problem. The backscattering caused by particles in turbid water can severely degrade the image. In this paper, A novel image dehazing method is devised, which combines optics with image processing technology. This method can directly remove part of the backscattering through polarization technology, and then quickly solve the dehazing image through our algorithm. Experimental results show that our method is valid and robust for targets of diverse materials, grayscale images and color images in diverse scattering environments. Furthermore, our method takes only 2 percent of the time of the fast dark channel prior (DCP) method when processing 1 K resolution images, which meets the requirements of real-time.

Execution time, reliability, and energy consumption are the three main performance parameters of processor scheduling for real-time embedded systems. It is very meaningful to optimize the energy optimization of processor scheduling to satisfy the requirements on time limit and reliability. This paper tries to optimize the energy consumption of the processor under three constraints: the partial ordering relations between task modules, the time limit, and the reliability. Based on directed acrylic graph (DAG) and quantum particle swarm optimization (QPSO), two scheduling algorithms were developed for the problem, namely, DAG_QPSO_I and DAG_QPSO_II. The two algorithms were compared with each other, and with other intelligent algorithms. The results show that the proposed algorithms are superior in optimization effect and efficiency, DAG_QPSO_I makes energy consumption more efficient than DAG_QPSO_II, and DAG_QPSO_II meets stricter requirements on time limit and reliability than DAG_QPSO_I.

Conventional energy consumption in refrigeration is one of the important reasons in global warming. Solar cooling systems are becoming more compact, having lower costs, and are potential alternative technologies, especially in hot and sunny climates. The adsorption, absorption, and dissociative evaporative cooling (DEC) are the technologies used for sorption machines, which are discussed in this review paper. Various multi-criteria performance indicators appearing in the previous studies are discussed, followed by evaluating the benefits and drawbacks of distinguishing sorption solar thermal cooling systems. Market research is conducted to prove the capacity of these technologies. The review shows that compared to other technologies, the solar absorption system is more efficient, so it is very commonly used for cooling applications in various locations. An important topic was also presented using phase change materials and nanofluids in solar sorption systems, that need more research. It is noted from this that most solar cooling systems are hybrid in terms of source and there are multiple applications (cooling - heating and electricity generation). The software packages to study these systems are introduced. Further research is required on desiccant materials that can be regenerated under lower temperatures that would augment the extent of solar desiccant cooling application.

High Blood Pressure (BP) is one of the most dangerous and widespread diseases due to which uncontrolled BP increases the risk of health problems that affects many body organs. Unfortunately, accurate BP measurements require several medical devices and a specialist person who has experience in BP measurement and measured in separate times. Thus, this paper proposes a simple calibration-free method to estimate BP by training BP and Photoplethysmography (PPG) data signals on a machine learning (ML) regression model. The proposed method overcomes drawbacks of BP measurement accuracy and provides enough capability for reliable and calibration-free BP estimation. The obtained results clarify that the error standard deviation (STD) is about 5.3 and 6.4 mmHg of systolic pressure (SP) and diastolic pressure (DP), respectively. In addition, the mean absolute error (MAE) is about 4.2 and 4.5 mmHg of SP and DP, respectively. These results achieve grade “A” for both SP and DP based on the Britain Hypertension Society (BHS) standard. Finally, the results of BP estimation regression models meet the International Organization for Standardization (ISO) requirements for non-invasive BP devices and consequently they can be utilized later in life experiments.

Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale global optimization and search problems. Teaching Learning based Optimization (TLBO) is one of the most recently developed EA. TLBO employs a group of learners or a class of learners to perform global optimization search process. The framework of the TLBO consists of two phases, including the Teacher Phase and Learner Phase. The Teacher Phase’ means learning from the teachers and the Learner Phase means learning through interaction among learners. In this paper, we have developed a hybrid TLBO (HTLBO) with aim at to further improve the exploration and exploitation abilities of the baseline TLBO algorithm. The performance of the proposed HTLBO algorithm examined upon using recently designed benchmark functions for the special session of the CEC2017 problems. The experimental results of the proposed algorithm are better than some well-known evolutionary algorithms in terms of proximity and diversity.

This paper proposes a High Performance Cuckoo Search Algorithm (HPCSA) for determining suitable operation parameters of the optimal wind-hydro-thermal system scheduling (OWHTSS) problem. The objective of the problem is to reach the lowest electricity generation cost of thermal power plants (TPPs) and wind power plants (WPPs) while exactly meeting all constraints of TPPs, WPPs and hydroelectric plants (HEPs). HPCSA is formed by applying improvements on the two main techniques of original Cuckoo Search Algorithm (CSA) to cover CSA’ drawbacks such as searching random solution spaces, always using two random solutions for getting a jumping step and suffering from slow convergence. HPCSA accompany with CSA, Adaptive CSA (ACSA), Snap-Drift CSA (SDCSA) and Water Cycle Algorithm (WCA) are run for solving four test systems in which the largest and complicated system is comprised of four TPPs, four HEPs and two WPPs with the uncertain wind feature. The result comparisons indicate that HPCSA is superior to applied and previous methods, and other modified versions of CSA in the literature in terms of better cost, higher stability, faster search ability and higher success rate. As a result, it leads to a conclusion that HPCSA is a strong metaheuristic algorithm for solving OWHTSS problem.

In the era of high-quality development, the sustainable development of manufacturing industry has shown characteristics of the Er-xiang Dual. In this paper, we mainly adopt the Er-xiang Dual theory to construct the theoretical framework on the sustainable development of the manufacturing industry, and deduced the indexes of the sustainable development in the manufacturing sector, including the state index, process index, coordination index and comprehensive index. Subsequently, we have used the cross-sectional data of average for 2016–2018 to carry out an empirical analysis in the manufacturing sector in 30 provinces of China, and divided these 30 provinces into four echelons according to the comprehensive index. Finally, we have put forward four kinds of development strategies, namely, “creating demonstrations”, “making up for the points of weakness”, “enhancing the industrial advantages” and “cultivating the development features”. This study has enriched the theoretical framework of the sustainable development in the manufacturing sector in the era of high-quality development from the perspective of system theory, which will provide vital references to accurately assess the level of the sustainable development in the manufacturing industry in various regions of China and to formulate classified development strategies.

The anti-seepage wall project is an important part of the anti-seepage system of water conservancy and hydropower projects, and it is the basic facility and barrier to resist floods and ensure the safety of people's lives and property. This paper analyzes the wireless communication GPRS (General Packet Radio Service) technology. After establishing a mathematical model and determining the optimal sensor deployment scheme, the mesh-type sensor network type is determined, and the overall design scheme of the system is analyzed. The wireless network completes the field information collection and instruction transmission, and transmits the field information to the remote monitoring platform for unified processing. In order to effectively improve the node coverage of wireless sensor networks, a network coverage optimization method based on a hybrid strategy to improve the Antlion algorithm is proposed. The continuity boundary shrinkage factor is used to improve the search traversal of the algorithm and accelerate the convergence speed. Dynamic weight coefficients are introduced during the elitization phase to balance the global exploration and local development capabilities of the algorithm. Combining the precocious convergence judgment mechanism and the dynamic hybrid mutation method, the algorithm can effectively jump out of the local optimum. The comparison experiments on 4 benchmark functions verify the effectiveness of the improved strategy. The water injection test was used to detect and evaluate the integrity and impermeability of the plastic concrete impervious wall. The drilling and excavation verification results confirmed the effectiveness and practicability of the method.

With the development of mobile communications and Internet of things (IoT), the Internet of vehicles (IoV) has become commonplace. To integrate the heterogenous IoV networks, this paper mainly explores the heterogenous network selection mechanism for the IoV. Considering the diversity of IoV networks, the authors proposed a heterogenous network selection algorithm based on comprehensive weight. Based on different service scenarios and user preferences, the proposed algorithm obtains the subjective weights of candidate networks, calculates the objective weights of judgement indices by entropy method, and combines the subjective and objective weights into a comprehensive weight. On this basis, the candidate networks were analyzed by the utility function (UF), and the optimal network was selected for access. The research results provide a reference for high-quality access to heterogenous networks.

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

This paper uses wavelet neurons instead of traditional neurons, and uses wavelet multi-resolution analysis to decompose the FPGA image scan of the ship engine. Because the neural network has the approximation ability of arbitrary functions, the wavelet transform is connected to the neural network to form a wavelet neural network. To test ship engines. The hardware design of GigE image acquisition and processing system based on FPGA was started. FPGA was used as the main control chip and Gigabit Ethernet was used as the transmission medium. The hardware circuit of the image data acquisition and image processing system was designed. It mainly includes the FPGA main control circuit and the FPGA Peripheral circuits. The high-speed image acquisition, transmission, storage, and display module circuit design is realized. Real-time monitoring and fault analysis of the engine's condition is performed by the FPGA image scanning method, and data of the engine's running state is pre-processed with the help of step tracking technology to make it a standard signal. The data is transmitted to the computer through NI's data acquisition card. Combining feature extraction such as information entropy, Fourier transform, EMD and wavelet neural network technology. The accuracy of the diagnosis results and the actual fault state is improved. It can enable the staff to monitor the running status of the engine in real time, improve the efficiency of engine fault diagnosis, reduce labour costs and maintenance costs, and thus realize intelligent, real-time and accurate status monitoring of the engine.

Rapid detection of distortions formed in the welds of metal has high importance to prevent disasters. Radiography images of the weld have an unlimited number of small defects with diverse shapes. Visual inspection of the weld is a complicated, time-consuming task; and depends on the observers' experience. Many computer-aided detection techniques have emerged as an alternative tool for segmenting the flaws of the weld, but none of them could segment alone all types of weld defects. In this paper, we attempt a possible solution and a novel image-based approach to solve the problem of weld defect detection using a data set of X-ray images. We aim to subtract the background and all horizontal defects and segment the non-horizontal flaws. For that, we apply some morphological operations and wiener filter to enhance the image quality; we use two novel filters to segment the non-horizontal defects; finally, we perform a post-processing operation. Our method depends on two convolution processes between the designed filters and the original image to achieve the segmentation process. We tested our approach on a universally available database of 68 images of the weld. Our method achieved high segmentation accuracy with zero errors. We highlighted the academic advancement of our technique by comparing its performance with other methods. Our efficient approach is effortless, applicable in practical segmentation processes of the defects of the weld. In the future, we will dedicate our work to the segmentation of horizontal defects by altering the shape of the two filters. Our method achieves satisfying accuracy; so, it is promising for weld defect detection. The particular contribution of our technique is that it can segment alone all types of weld defects, contrary to traditional weld defect detection methods. The computing time is optimized compared to other algorithms.

Based on the panel data of 30 Chinese provinces in 2000–2018, this paper measures the green total factor energy efficiency (GTFEE) of China with the super-slack-based measure (Super-SBM) model. Then, the spatial correlation of provincial GTFEEs was analyzed, a spatial econometric model was established based on the panel data, and was used to empirically verify the effect of financial agglomeration, technological innovation, and financial agglomeration and technological innovation interaction on GTFEE. The results show that: in the sample period, most Chinese provinces had a low GTFEE, and the low-GTFEE provinces mainly belong to inland area; only a few eastern coastal provinces achieved a satisfactory GTFEE. China and its three regions exhibited similar trends of GTFEE. By mean GTFEE, eastern region ranked the highest, followed by central region; the western region came at the bottom. The Global Moran’s I values show strong spatial correlations between provincial GTFEEs, that is, adjacent provinces often have similar GTFEEs, and the provinces tend to cluster in terms of GTFEE. According to the spatial econometric model, GTFEE is significantly inhibited by financial agglomeration, but significantly promoted by technological innovation. GTFEE is not clearly affected by financial agglomeration and technological innovation interaction. Opening up has a significant positive impact on GTFEE, while industrial structure, energy structure and environmental regulation have a significant negative impact on GTFEE.

The aim of this paper is providing a detailed review for the applications of phase change materials (PCMs) in residential heating. The study focuses mainly on its use in domestic water heating systems. Different studies accounting for structural characterization, research methodology, and long term performance were carefully assessed. The gaps in the literature have been highlighted and recommendations for future studies have been presented. The technical gaps urge the need for research to be directed towards enhancing PCM properties in the domestic system, novel integration of PCM within the system, and optimization of system performance based on real-time accurate weather data. The economic opportunities for such systems were presented in different locations of the world by investigating different energy efficiency measures. The work presented herein identifies potential energy management opportunities that can significantly reduce our reliance on fossil fuels. Consequently, promoting a green future and mitigating greenhouse gas emissions. It is structured to provide guidance for researchers and engineers working in inclusion of PCMs in residential heating applications.

Water policy decisions are sensitive in countries like Egypt suffering from water shortage, such decisions directly affect the development in all sectors. The problem of not linking water policy decisions to trade priorities and water security is negatively affecting the efficient water use of the country. In this regard, this paper’s objective is to study and evaluate water resources utilization in Egypt as case study using the footprint process compared to available water resources performing detailed analysis of the effect of considering water footprint approach in developing water policies and water strategies. Egypt’s water footprint was calculated in novel methodology merging different international and national data, then compared to water footprint of different water uses. The results showed that the average Egyptian national water footprint for the years (2012–2016) was 111.05 billion m3, while the average available water resources was 75.66 billion m3, and the average water deficit was 47% of the available water resources and 32% of the total national water footprint. These findings necessitate the need to integrate water resources management policies, agricultural and trade policies to feed in a comprehensive country water accounting system.

Text classification (TC) is an approach used for the classification of any kind of documents for the target category or out. In this paper, we implemented the Support Vector Machines (SVM) model in classifying English text and documents. Here we did two analytical experiments to check the selected classifiers using English documents. Experimental results performed on a set of 1033 text document present that the Rocchio classifier provides the best performance results when the size of the feature set is small while SVM outperforms the other classifiers. From the experimental analysis, we observed that the classification rate exceeds 90% when using more than 4000 features.

The real-time rendering of large-scale terrain scenes has been a challenge and is a popular research topic in the fields of 3D games and virtual reality. To fully utilize GPU features, this paper proposes a real-time rendering algorithm for large-scale regular grid terrain based on GPU Tessellation. The CPU processes a coarse-grained quadtree to generate terrain patches and then passes the patches to the GPU after view culling. Then, the GPU refines the terrain by using GPU Tessellation and displacement mapping. The most important advantage of our method is that during the tessellation stage, a new rule that the distance, screen space projection error and variance of the height, as the standard of terrain roughness, is designed. During the tessellation stage, the distance, screen space projection error and variance of the height, as the standard of terrain roughness, are considered. The experimental results show that our algorithm can greatly reduce the CPU processing time and main memory space requirement, and fully utilize the new features of the GPU; In conclusion, the proposed approach can render large-scale terrain with high-precision in real-time.

This paper presents a novel method for determining the worm wheel tooth flank surface. The worm wheel model is developed according to the theory of gearing and the gear generation mechanism. Two cases are considered simultaneously. In one case, the worm wheel is generated by the hourglass worm hob with a rectilinear tooth axial profile, while in the other case, it is generated with an arc (concave and convex) tooth axial profile. The contact lines between the hourglass worm hob and worm wheel surfaces are determined. Surface formation generated during processing by the tool external edge and formation so-called region II was presented. The manner of separating regions on the worm wheel tooth flank surface was developed. Because the difference between the parameters that describe the tool side surface, only the flowchart of the algorithm for determining the worm wheel tooth side surface of rectilinear profile is presented. For the arc profile, the algorithm flowchart is analogous. The proposed procedures and the developed computer-aided worm wheel generating program allow obtaining complete mathematical representation of worm wheel tooth flank surface.