Study on the Safety of Tunnel Structure Vibration in Dry Ice Powder Thermal Shock Rock Breaking

Xiaofei Wang,Shaobin Hu,Enyuan Wang 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.11

The application of explosive blasting for rock breaking works was not possible in densely populated areas, which caused problems in urban tunnel construction. In the application of dry ice powder thermal shock breaking in tunnel excavation, a vibration monitoring system was employed to monitor the vibration response of the tunnel structure during the breaking process, and it was used to extract the signal characteristics by Hilbert-Huang transformation. Experimental results are as follows: 1) The peak vibration speed was below 50 mm/s for concrete structures safe as long as it was farther than 10 m from the burst hole in the support concrete. 2) Aggregate decay of vibration velocities caused by thermal shock breaking of rock with dry ice powder corresponded to the decay law of the power function. The range of influence of the vibration was considerably smaller compared to that of drilling and blasting the rock; 3) The new rock-breaking technology induced damage can be divided into three regions, with radial fractures generated by high-energy fluids dominating. 4) Hilbert Huang transform extracted signal features more objectively and accurately, with excellent reference for the safety monitoring of tunnel structures.

Study of BP and RBF Neural Networks Applied to the Prediction of Vibration Characteristics in Static Blasting of Dry Ice Powder

Xiaofei Wang,Shaobin Hu,Enyuan Wang 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.4

The novel dry ice powder static blasting rock breaking lacked working standards, especially for the vibration safety assessment of the construction site for the protection of the building structure, in comparison with the traditional drill and blast method which had a proven operational process and safety specifications. The Sadovsky vibration velocity prediction formula could only predict the vibration velocity and was project specific. Oscillation parameters that needed to be considered in the vibration safety assessment, such as the dominant frequency of vibration, could not be obtained through empirical formulas. Using the five parameters of hole depth, blast center distance, dry ice powder mass and rock classification as the main influencing factors, BP and RBF neural network models were constructed by Matlab software to predict the peak vibration velocity, main frequency and maximum displacement of dry ice powder blasting. Projection results revealed that it is structurally simpler than the BP neural network and that the RBF was more accurate in predicting the target than the BP network. The results of the study had significant implications for the safe application of the new technology, and more samples of field data need to be obtained in the future, along with the use of more advanced predictive modelling.

Segmented Douglas-Peucker Algorithm Based on the Node Importance

( Xiaofei Wang ),( Wei Yang ),( Yan Liu ),( Rui Sun ),( Jun Hu ),( Longcheng Yang ),( Boyang Hou ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4

Vector data compression algorithm can meet requirements of different levels and scales by reducing the data amount of vector graphics, so as to reduce the transmission, processing time and storage overhead of data. In view of the fact that large threshold leading to comparatively large error in Douglas-Peucker vector data compression algorithm, which has difficulty in maintaining the uncertainty of shape features and threshold selection, a segmented Douglas-Peucker algorithm based on node importance is proposed. Firstly, the algorithm uses the vertical chord ratio as the main feature to detect and extract the critical points with large contribution to the shape of the curve, so as to ensure its basic shape. Then, combined with the radial distance constraint, it selects the maximum point as the critical point, and introduces the threshold related to the scale to merge and adjust the critical points, so as to realize local feature extraction between two critical points to meet the requirements in accuracy. Finally, through a large number of different vector data sets, the improved algorithm is analyzed and evaluated from qualitative and quantitative aspects. Experimental results indicate that the improved vector data compression algorithm is better than Douglas-Peucker algorithm in shape retention, compression error, results simplification and time efficiency.

Multiple Feature Voting based Human Interaction Recognition

Xiaofei Ji,Changhui Wang,Xinmeng Zuo,Yangyang Wang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1

A Survey of Green Mobile Networks: Opportunities and Challenges

Wang, Xiaofei,Vasilakos, Athanasios V.,Chen, Min,Liu, Yunhao,Kwon, Ted Taekyoung Springer-Verlag 2012 MOBILE NETWORKS AND APPLICATIONS Vol.17 No.1

무선 메쉬 네트워크에서의 다중 트래픽 흐름을 위한 종단간 처리량 모델링 및 효율적인 라우팅 경로 선택 기법

왕효비(Xiaofei Wang),권태경(Ted Taekyoung Kwon),최양희(Yanghee Choi) 한국정보과학회 2010 정보과학회논문지 : 정보통신 Vol.37 No.4

무선 메쉬 네트워크는 최근 주목받는 연구 주제로 부상하고 있다. 무선 메쉬 네트워크는 대규모 무선 랜 장치와 AP들이 서로 연결된 무선 기반 구조로, 무선 메쉬 네트워크의 처리량(throughput)을 향상시키는 것을 목표로 한 수많은 연구가 이루어진 바 있다. 여기에서는 동시에 생겨나는 다수의 트래픽 흐름들을 전송하기 위한 적절한 라우팅 경로를 설정하는 작업이 매우 중요하다. 본 논문에서는 IEEE 802.11 DCF하에서 전송되는 다수의 트래픽 흐름에 대해 신호 감지(Carrier Sensing), 간섭 등의 정보를 이용하여 양 말단간 (end-to-end) 처리량을 수학적으로 모델화하기 위한 방법을 제시하고자 한다. 각 단말에 성공적으로 이루어지는 평균 서비스 시간을 비교하고, 한편으로는 트래픽 흐름 가운데 병목현상이 발생하는 부분을 찾아내어 그로부터 말단간 처리량의 최대치를 계산할 수 있다. 본 논문에서 제시된 모델을 사용하여 동시에 전송되는 다수의 트래픽 흐름을 전달하기 위한 전송 경로에 대한 후보 경로를 얻어낼 수 있으며, 얻어진 경로로부터 처리량을 최대로 높일 수 있는 효율적인 경로를 찾아낼 수 있다. 제시된 모델링 기법과 최적 경로 선택 메커니즘은 무선 메쉬 네트워크에서의 다양한 트래픽 흐름을 사용한 시뮬레이션을 통해 평가하였다. Wireless Mesh Networks (WMNs) have gained a lot of attention recently. Based on the characteristic of WMNs as a highly connected wireless infrastructure, many efforts from research organizations are made in order to improve the performance of the flow throughput in WMNs. Therefore, it is very critical issue to establish efficient routing paths for multiple concurrent ongoing flows. In this paper, we propose a general modeling methodology to analyze the end-to-end throughput of multiple concurrent flows by analytical calculation taking into account the carrier sensing behaviors, interference and the IEEE 802.11 Distributed Coordination Function mechanism. After the comparison of the average service time for each successful transmission at each node, we analyze the bottlenecks of flows, and hence obtain the maximum end-to-end throughput of them. By using our proposed model, it is possible to predicate the throughput of several candidate routing paths for multiple concurrent ongoing data flows, so we can select the most efficient route that can achieve the highest throughput. We carry out simulations with various traffic patterns of multiple flows in WMNs to validate our modeling and our efficient route selection mechanism.

Application of artificial intelligence for solving the engineering problems

Xiaofei Liu,Xiaoli Wang 국제구조공학회 2023 Structural Engineering and Mechanics, An Int'l Jou Vol.85 No.1

Using artificial intelligence and internet of things methods in engineering and industrial problems has become a widespread method in recent years. The low computational costs and high accuracy without the need to engage human resources in comparison to engineering demands are the main advantages of artificial intelligence. In the present paper, a deep neural network (DNN) with a specific method of optimization is utilize to predict fundamental natural frequency of a cylindrical structure. To provide data for training the DNN, a detailed numerical analysis is presented with the aid of functionally modified couple stress theory (FMCS) and first-order shear deformation theory (FSDT). The governing equations obtained using Hamilton’s principle, are further solved engaging generalized differential quadrature method. The results of the numerical solution are utilized to train and test the DNN model. The results are validated at the first step and a comprehensive parametric results are presented thereafter. The results show the high accuracy of the DNN results and effects of different geometrical, modeling and material parameters in the natural frequencies of the structure.

Optimizing location of particle damper using principles of gas-solid flow

Xiaofei Lei,Chengjun Wu,Peng Chen,Hengliang Wu,Jianyong Wang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.6

Particle damping is a passive control technology with strong nonlinearity whose damping effect is relative to the vibration intensity where a particle damper is installed. Then, seeking the optimal installing location of the particle damper to improve the damping effect and vibration control performance is an important research project. To this problem, bound optimization by quadratic approximation (BOBYQA) was employed to discuss the optimal location of a particle damper at the both fixed end plate. For theoretically evaluating the damping effect and invoking it into BOBYQA, the principle of gas-solid flow was used to study the damping effect and establish the theoretical model of particle damping. Further, the estimation precision of the mathematical model was verified by experiment; the results indicate that the proposed mathematical model can more accurately predict the dynamic response of a particle damper installed at both fixed end plate. Therefore, a mathematical model was employed to discuss the optimal position of the particle damper for minimizing maximum amplitude (MMA). The results indicate that particle damper should be installed at the model top close to the monitoring point; if there are two resonances whose amplitudes are equivalent or approximate, the particle damper should be installed at the junction of these model tops.

Robust adaptive model selection and estimation for partial linear varying coefficient models in rank regression

Xiaofei Sun,Kangning Wang,Lu Lin 한국통계학회 2018 Journal of the Korean Statistical Society Vol.47 No.1

Partial linear varying coefficient models are often used in real data analysis for a good balance between flexibility and parsimony. In this paper, we propose a robust adaptive model selection method based on the rank regression, which can do simultaneous coefficient estimation and three types of selections, i.e., varying and constant effects selection, relevant variable selection. The new method has superiority in robustness and efficiency by inheriting the advantage of the rank regression approach. Furthermore, consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies also confirm our method.

Size-Controlled Silver Nanoparticles Confined in Ordered Mesoporous Silica and Their Enhanced Catalytic Activities

Xiaofei Zhou,Jiawei Wan,Yefeng Liu,Di Liu,Hong Wang,Xiaoyong Lai,Yanzhao Zou,Guo Lin,Jian Chen 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2017 NANO Vol.12 No.9

A cooperative assembly route has been developed, by which silver nanoparticles with controlled sizes are incorporated into the channels of ordered cubic mesoporous silica (KIT-6) with different pore sizes (4.3–6.4 nm). The samples were characterized by XRD, TEM, FT-IR, UV-Vis and N2 physisorption. The pore wall of mesoporous silica can efficiently confine the growth of silver nanoparticles within the channels and their average sizes decreased with the pore size reduction of KIT-6. Catalytic activities of the resultant Ag/KIT-6-x (x stands for hydrothermal temperature) composite for reducing harmful organic dye Rhodamine B (Rh B) by sodium borohydride (NaBH4) were investigated. All the Ag/KIT-6 composite samples show great catalytic activities, among which Ag/KIT-6-80 with higher loading and smaller size of Ag nanoparticles exhibits higher catalytic activity than those of Ag/KIT-6-60 and Ag/KIT-6-100.