Editor in chief：朱立光
International standard number：ISSN 1008-1542
Unified domestic issue：CN 13-1225/TS
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2020, 41(1):1-13. DOI: 10.7535/hbkd.2020yx01001
Abstract:Superhydrophobic surface can cause a water droplet to show a static contact angle >150° and a sliding angle <10°, which is widely used in many engineering fields, such as self-cleaning, anti-corrosion, hydrophobic ice suppression and ship drag reduction. Based on bionic engineering principle, scholars have comprehensively researched many typical superhydrophobic bionic prototypes, with the purpose of obtaining the theoretical basis for the superhydrophobic surface development. In this review, based on the typical prototypes (animals and plants) of showing the phenomena of superhydrophobic wettability, the effect of morphology/structure characteristics on the superhydrophobic wettability properties is reviewed, and mathematical models used to quantify superhydrophobic wettability of material surface are introduced. We especially focus on the recent progress in bionic preparation technology of superhydrophobic surface, mainly including traditional preparation methods and 3D printing technology, and wettability quantification of fabricated superhydrophobic surface. Meanwhile, we point that low cost, large area and durable effect of bionic superhydrophobic surface are the important development direction in near future. This review enriches the further understanding of superhydrophobic wettability, and promotes the development of new ideas, new methods and new technologies in superhydrophobic surface preparation.
2020, 41(1):14-22. DOI: 10.7535/hbkd.2020yx01002
Abstract:In order to effectively reduce nitro compounds in contaminate water and produce hydrogen from water, Ag/mpg-C3N4 nanocomposite particles are prepared by impregnation method, and 5-OH-ZnTPP is loaded on the surface of Ag/mpg-C3N4 to prepare Ag/5-OH-ZnTPP/mpg-C3N4 composite particles. The morphology, microstructure and composition of as-prepared nanocomposite particles are characterized by BET, SEM, XRD and XPS. The results show that the mpg-C3N4 is lamellar structure with porous, while the silver nanoparticles are dispersed on the surface or between the lamellar of the mpg-C3N4. The photoelectric property of the Ag/5-OH-ZnTPP/mpg-C3N4 nanocomposite particles is also characterized by UV-vis DRS, PL and PT, respectively. The results reveal that the visible-light utilization of mpg-C3N4 are improved by the introduction of silver nanoparticles and the sensitization of porphyrin. The catalytic activities are examined by using the reduction of 4-NP and the photocatalytic H2 production as model reactions, which demonstrates that the photocatalytic activity of mpg-C3N4 is enhanced after modified by silver nanoparticles and sensitized with dye porphyrins. Ag/5-OH-ZnTPP/mpg-C3N4 nanocomposite particles have high catalytic activity for the reduction of 4-NP and hydrogen production.
2020, 41(1):23-30. DOI: 10.7535/hbkd.2020yx01003
Abstract:In order to improve the output voltage quality of single-phase PWM rectifier, solve the shortcomings of parameter setting difficulties in traditional control strategies, and improve the input voltage of the intermediate stage of small energy routers, a voltage external loop fuzzy adaptive control strategy is proposed. Firstly, according to the circuit characteristics, the optimal mathematical model of single-phase PWM rectifier is established. Secondly, the idea of fuzzy control is introduced into the voltage outer loop control strategy. Combined with the traditional PI control, the fuzzy rules are constructed according to the influence of traditional PI parameters. The fuzzy factor is calculated. Finally, combined with the proportional resonance control, the fuzzy adaptive control of the voltage outer loop of the single-phase PWM rectifier is given. The experimental results show that the proposed control strategy has good dynamic characteristics, which can reduce the fluctuation of output voltage when the load fluctuates to a certain extent, and can realize the rectification or inversion of the rectifier under the unit power factor condition. It is in line with the two-way transmission requirements of the small energy router.
2020, 41(1):31-39. DOI: 10.7535/hbkd.2020yx01004
Abstract:Aiming at the currently problem that data dissemination protocols in opportunistic networks are mainly based on single-source and multi-destination model, a multi-source and multi-destination data dissemination model for opportunistic networks is proposed, and an interest-and coding-aware (ICA) data dissemination protocol is designed.First, relays encode the different data flows which satisfy the same interest via inter-flow random linear network coding and then forward them. Second, nodes sharing the same interest exchange their interest coding data with each other. Once receiving enough independent coding packets which satisfy the same interest, the nodes decode the coding packets and obtain the original interest data. At last, ONE simulation is conducted for the multi-source and multi-destination data dissemination. The result shows that compared with the data dissemination protocol based on ER, ICA can obtain lower delay while consuming fewer buffers, less network bandwidth and network cost. The research result provides a feasible and efficient solution for opportunistic networks data dissemination mechanism.
2020, 41(1):40-49. DOI: 10.7535/hbkd.2020yx01005
Abstract:In order to solve the problem that the intelligent evacuation system in large-scale integrated buildings can plan a reasonable and safe evacuation path according to the complex building structure when the fire occurs, the multi-start multi-egress path planning method based on the improved A* algorithm is proposed. By increasing the turning penalty value, the real-time information of the fire affected area, and the distance of the fire center point are improved to improve the valuation function, and then the optimal evacuation path is calculated. According to the searched optimal path, the direction of each guiding mark in the three-dimensional floor map is adjusted, thereby guiding people at different positions to evacuate from the optimal path. The simulation experiments show that compared with the traditional Dijkstra algorithm and A* algorithm, the improved A* algorithm in calculating multiple starting points and multi-export evacuation paths has a clearer search direction, a smoother path, and a higher search efficiency and shorter running time, and combines fire information to ensure evacuation path security. The algorithm has a good application prospect in solving the fire safety evacuation path of large-scale integrated buildings.
2020, 41(1):50-57. DOI: 10.7535/hbkd.2020yx01006
Abstract:Aiming at the problems that the speed balance overshoot is large, the adjustment time is long, and the speed balance effect is poor during the walking process of a wheeled robot, a four-wheel differential motion model of a wheeled robot is established with Matlab/Simulink and Carsim simulation software, and for the brushless direct current motor system(BLDCM) system, based on the original fuzzy PID, combining with the anti-integration saturation algorithm and the variable speed integral algorithm, an speed regulation method based on improved fuzzy PID is proposed. The simulation results show that compared with the traditional fuzzy PID controller, the fuzzy PID controller improved by the anti-integral saturation and variable speed integration algorithm reduces the overshoot by 30% and the adjustment time by 33%. The response time is short and the speed response curve has small fluctuations. The wheeled robot experimental verification platform is built. The experimental results show that the speed regulation method based on improved fuzzy PID has fast speed response and meets the requirements of wheeled robot speed control. The proposed design may provide some theoretical reference for speed stable system debugging of wheeled detection robot, and can be used in control systems with speed control as main purpose.
2020, 41(1):58-65. DOI: 10.7535/hbkd.2020yx01007
Abstract:In order to investigate the microstructure and toughness of low carbon chromium molybdenum nickel bearing steel under different heat treatment processes, an austenitic isothermal insulation test is performed. The effects of heating temperature and holding time on the austenite grain size, carbides and impact energy of low-carbon chromium-molybdenum-nickel bearing steel are studied. The results show that when the austenitizing temperature is not higher than 1 070 ℃, the carbides in the steel do not dissolve significantly, the area percentage of carbides is 1.93%, the austenite grain growth is not obvious, the average grain size is 49 μm, and the impact energy absorption is greater than 50 J. When the austenitizing temperature is 1 080 ℃ or higher, the carbide area percentage is 1.23%, while the carbide area percentage is 0.16% at 1 090 ℃, and the carbides dissolve in large quantities, resulting in a nailing effect and bigger austenite grains, and the impact energy absorption drops sharply to less than 30 J. When the holding time is longer than 45 min, the grain size tends to be stable. According to the test results, a grain growth model of the test steel heated at 1 050~1 090 ℃ for 15~60 min is provided, which can provide theoretical basis for the design of the heat treatment process for the steel.
2020, 41(1):67-75. DOI: 10.7535/hbkd.2020yx01008
Abstract:With the rapid development of urbanization and industrialization, the problem of air pollution has become increas-ingly prominent, and air quality prediction is particularly important. Some representative studies currently monitor and forecast air quality in real time. For example, ZHOU Guangqiang et al. Used numerical prediction to analyze air quality in eastern China. However, experimental results show that this method is difficult to predict and is very important. SANKAR et al. Used multiple linear regression to predict air quality, but the experimental results showed that the linear model had low prediction accuracy and slow efficiency;PREZ et al. Used statistical methods to predict air quality, and the experimental results proved the prediction accuracy of the statistical method relatively low; WANG et al. Used an improved BP neural network to establish a prediction model for the air quality index, and their experiments verified that the BP neural network has a slow convergence rate and is prone to fall into the local optimal solution problem; YANG et al. Air quality concentration effect, a PM2.5 concentration prediction model based on random forests was established, and the empirical process proved that the meshing program weakened the quality and efficiency of subsequent air quality analysis; these methods are difficult to model from a time perspective, and the prediction accuracy is low is a more important issue. Because low prediction accuracy may lead to large errors in air quality prediction results. 河北科技大学学报 2020年 第1期 张冬雯，等：基于长短期记忆神经网络模型的空气质量预测 In this paper, a neural network model based on long -term memory (LSTM) is proposed to solve the problem of low prediction accuracy in air quality research.MAPE, RMSE, R, IA and MAE were used to test the predictive performance of LSTM neural network and the comparison model.Since Delhi and Houston are cities with high levels of air pollution, the experimental data sets used in this paper were from the air quality data of Punjabi Bagh monitoring station in Delhi from 2014 to 2016 and the air quality data of Harris County monitoring station in Houston from 2010 to 2016.By comparing LSTM neural network with multiple linear regression and regression model (SVR), the experimental results show that LSTM neural network is suitable for time series prediction with multiple variables or multiple inputs LSTM neural network has the advantages of high prediction accuracy, high speed and strong robustness.
2020, 41(1):76-87. DOI: 10.7535/hbkd.2020yx01009
Abstract:With the rapid development of the internet, how to mine and analyze massive network information has become a recognized hot and difficult problem. Among them, the recommendation system can provide users with accurate and fast business (commodities, projects, services, etc.) information, which is the common interest and research hotspot of industry and academia in recent years. A recommendation system can help users to solve the problem of information overload when there is no clear demand or a large amount of information. However, at present, the types of data are diverse and the application scenarios are extensive. When faced with this situation, the recommendation system also encounters challenges such as cold start and sparse matrix. Deep learning is an important research field and the most important branch of machine learning. In recent years, deep learning has developed rapidly. Researchers have made great breakthroughs and achievements in speech recognition, image processing, natural language processing and other fields by using deep learning. At present, deep learning has also been favored by a large number of researchers in the field of recommendation and has become a new direction. Incorporating deep learning technology into the recommendation method can effectively solve the problems of cold start and sparse matrix in traditional recommendation systems, and improve the performance and recommendation accuracy of the recommendation system.This paper mainly summarizes the application of traditional recommendation methods and the application of neural network in current deep learning technology in recommendation methods, among which the traditional recommendation methods can be divided into the following three categories: 1) Content-based recommendation methods is mainly based on the feature information between the user and the project. The connection between users will not affect the recommendation result, so there is no problem of cold start and sparse matrix, but the content-based recommendation results are low in novelty and face the problem of feature extraction. 2) The collaborative filtering recommendation method is the most widely used method that does not require information about users or items, but only makes accurate recommendations based on the user's interactions with items such as clicks, views, and ratings. Although this method is simple and effective, sparse matrix and cold start problems will occur. 3) The hybrid recommendation method combines the characteristics of the first two traditional recommendation methods and can achieve good recommendation effect. However, this method still faces some challenges and difficulties in processing multi-source heterogeneous auxiliary information such as text and images.Recommendation methods based on deep learning are mainly classified according to neural network categories, which are divided into the following four categories: Recommendation methods based on deep neural network (DNN); recommendation methods based on convolutional neural network (CNN); recommended methods based on cyclic neural network (RNN) and long and short term memory neural network (LSTM); and recommended methods based on graph neural network (GNN). Incorporating deep learning technology into the recommendation field, the constructed model has the following five advantages: it has strong representation ability, and can directly extract the characteristics of users and items from the content; with strong anti-noise ability, it can easily process data with noise; in deep learning, cyclic neural network can model dynamic or sequential data; it can learn user or project characteristics more accurately; and deep learning facilitates the unified processing of data and can process large-scale data. Applying deep learning technology to the recommendation field can effectively overcome the challenges faced by traditional recommendation methods and improve the recommendation effect.
2020, 41(1):88-98. DOI: 10.7535/hbkd.2020yx01010
Abstract:Software project is an engineering process that aims to meet the demand side of software scientifically, covering various elements such as personnel, technology, management, etc. For a long time, due to some uncertain factors of software projects, many problems such as design, cost, progress and function change are often encountered in the implementation of software projects. Although the development technology is constantly improving, the management problems always exist, and the risk problem is inevitable, making it being the focus of the software industry. Correctly understanding and managing the software project risk can improve the success rate of software project development and reduce the probability of risk occurrence. 河北科技大学学报 2020年 第1期 瞿〓英，等：基于文本分析的软件项目风险研究演化脉络解析 Software project risk management is one of the key issues in software project management. With the development of software industry, software project risk also presents new characteristics, and the content of risk management also has new changes. In order to track the context and development trend of software project risk management research, this paper adopts the following methods: using web crawler technology, 3 129 domestic and foreign software project risk related literature are obtained; using word segmentation and statistical analysis technology, the author, key words, main topics, etc. of the literature are extracted and word frequency analysis is carried out, and the spatial distribution of the research subjects is mined; from the perspective of the published paper number and time, this paper compares and analyzes the study of Chinese and foreign scholars, and combs out the evolution processes of software project risk concept definition, research stage, research methods, etc. Through the analysis of the literature topic, the paper summarizes the research direction and trend of software project risk. Through the above text analysis ideas, we can get the mainstream research methods and development trends in this field. It can be inferred from the change of literature number that there are many researches on software project risk homogenization, and it is difficult to explore the innovative breakthrough of the research, resulting in the decrease of the number of Chinese articles and stagnation. By observing the word cloud visualization of the research team, two main research directions are found: computer and management. How to conduct risk research from the perspective of management has always been the focus of this field. Based on the time sequence literature topic summary, it can be clearly seen that the software project risk research is a project management oriented and risk management-oriented research, and the project management and risk management are integrated to define the concept of software project as the main body. After the theoretical research of software project risk management is relatively mature, related technical researches such as risk assessment, risk identification and risk control begin to rise, then risk management validation research is conducted from the perspective of application. Up to now, risk management still takes assessment as the main research method. Through the co-occurrence analysis of high-frequency words, this paper can find relatively new research directions in lexical association. for example, the process of risk identification is transforming from artificial subjective identification to machine automatic identification, the transformation of attribute characteristics of risk management objects is from static to dynamic, and other cutting-edge researches such as the establishment of risk knowledge base caused by big data technology, the construction of intelligent risk management system, etc. The exploration of research trends will provide new solutions for software project risk prevention and control, improve the success rate of software projects, and provide reference methods for the related research of software project risk.
2020, 41(1):99-104. DOI: 10.7535/hbkd.2020yx01011
Abstract:In order to reduce the charge recombination and improve the performance of hybrid solar cells, the mixture of P3HT and Spiro-OMeTAD is used as the photoactive layer and hole-transport layer, and is spun onto TiO2 nanorod/Sb2S3 nanoparticles composite film to prepare a hybrid solar cell. By means of SEM, UV visible absorption spectrum, XRD, electrochemical impedance spectroscopy, and steady-state fluorescence spectrum and J-V curve, the microstructure and photovoltaic performance of the hybrid solar cell are characterized and tested. The results show that the hybrid solar cell with the mixture ratio of P3HT and Spiro-OMeTAD of 15 mg/1 mL has a lower charge recombination rate, a longer electron life and the power conversion efficiency is 4.57%.The prepared hybrid solar cell has excellent performance and good application prospect.