Electrochemical Polishing of Additively Manufactured Ti–6Al–4V Alloy

Yifei Zhang,Jianzhong Li,Shuanghang Che,Yanwen Tian 대한금속·재료학회 2020 METALS AND MATERIALS International Vol.26 No.6

In this present paper, the electropolishing behavior of Ti–6Al–4V alloy fabricated by additive manufacturing in chloridecontainingethylene glycol electrolyte was surveyed. The impacts of chloride ion on surface quality and oxide film ofTi–6Al–4V were analyzed in dependence on the surface topography, roughness, weight loss ratio and compositions. Thevisual and microscopic results revealed that the optimally electropolished surface was attained in a 0.4 mol L−1 chlorideelectrolyte with a decreased surface roughness of 75.04% and a weight loss rate of 4.93%. For lower (CCl−1 ≤ 0.3 mol L−1)or higher concentrations (CCl−1 ≥ 0.5 mol L−1), a smooth and flat surface was not observed due to insufficient reactions orexcessive anodic dissolution. During the electropolishing, the titanium oxides nucleated and corresponding surface tensionincreased, resulting in the formation of a stable TiO2film on the surface of the Ti–6Al–4V alloy, increasing the corrosionresistance of the specimen.

A Distributed Adaptive Mixed Self-/Event-triggered Formation Control Approach for Multiple Stratospheric Airships with Relative State Constraints and Input Delay

Yifei Zhang,Ming Zhu,Tian Chen,Zewei Zheng 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.1

This paper investigates the distributed formation control problem of multiple stratospheric airships in three-dimensional space with several practical problems, such as relative state constraints, input delay, input saturation and disturbances. An adaptive mixed self-/event-triggered formation control scheme is proposed by combining backstepping control, an adaptation technique and a mixed self-/event-triggered control mechanism. First, a novel relative-error-constraint virtual control law is designed based on the barrier Lyapunov function, which is processed into the desired velocity and angular velocity as the input of the next-step designed controller. Then, an adaptive controller is designed based on a designed adaptive law that is utilized to eliminate the influence of external disturbances, input saturation and input delay. In addition, a mixed self-/event-triggered mechanism is designed in the whole system, involving a self-triggered mechanism in the virtual control law and an event-triggered mechanism in the adaptive controller. All signals in the closed-loop system are proven to be semiglobal, uniform and ultimately bounded, and Zeno behavior is proven to be excluded. Finally, the effectiveness of the proposed method is verified through simulations.

RPTOR methylation in the peripheral blood and breast cancer in the Chinese population

Yin Yifei,Lei Shuifang,Li Lixi,Yang Xiaoqin,Yin Qiming,Xu Tian,Zhou Wenjie,Li Hong,Gu Wanjian,Ma Fei,Yang Rongxi,Zhang Zhengdong 한국유전학회 2022 Genes & Genomics Vol.44 No.4

Background: Altered regulatory-associated protein of mTOR, complex 1 (RPTOR) methylation levels in peripheral blood was originally discovered as breast cancer (BC)-associated risk factor in Caucasians. Objective: To explore the relationship between RPTOR methylation and BC in the Chinese population, we conducted two independent case-control studies. Methods: Peripheral blood samples were collected from a total of 333 sporadic BC cases and 378 healthy female controls for the DNA extraction and bisulfite-specific PCR amplification. Mass spectrometry was applied to quantitatively measure the levels of methylation. The logistic regression, Spearman's rank correlation, and Non-parametric tests were used for the statistical analyses. Results: In our study, we found an association between BC and RPTOR_CpG_4 hypomethylation in the general population (per-10% of methylation, OR 1.29, P = 0.012), and a weak association between BC and RPTOR_CpG_8 hypomethylation in the women with older age (per-10% of methylation, OR 2.34, P = 0.006). We also identified age as a confounder for the change of RPTOR methylation patterns, especially at RPTOR_CpG_4, which represented differential methylation comparing age groups especially in the BC cases (age < 50 years vs age ≥ 50 years by Mann-Whitney U test, P < 0.0001 for BC cases and P = 0.079 for controls). Conclusion: Our study validated the association between hypomethylation of RPTOR and BC risk in the Chinese population also with weak effect and mostly for postmenopausal women. In addition, our findings provided novel insight for the regulation of DNA methylation upon aging or the change of hormone levels.

Pointwise CNN for 3D Object Classification on Point Cloud

( Wei Song ),( Zishu Liu ),( Yifei Tian ),( Simon Fong ) 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.4

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

Wei Song,Shuanghui Zou,Yifei Tian,Su Sun,Simon Fong,조경은,Lvyang Qiu 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.6

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmannedground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surroundingterrain reconstruction crucially influences decision making in UGVs. To increase the processing speed ofenvironment information analysis, we develop a CPU-GPU hybrid system of automatic environmentperception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists ofthree functional modules, namely, multi-sensor data collection and pre-processing, environment perception,and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processingfunction registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motioninformation into a global terrain model after filtering redundant and noise data according to the redundancyremoval principle. In the environment perception module, the registered discrete points are clustered intoground surface and individual objects by using a ground segmentation method and a connected componentlabeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversedand obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibratesthe projection matrix between the mounted LiDAR and cameras to map the local point clouds onto thecaptured video images. Texture meshes and color particle models are used to reconstruct the ground surfaceand objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallelcomputation method to implement the applied computer graphics and image processing algorithms in parallel.

A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform

( Wei Song ),( Ning Feng ),( Yifei Tian ),( Simon Fong ),( Kyungeun Cho ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

Song, Wei,Zou, Shuanghui,Tian, Yifei,Sun, Su,Fong, Simon,Cho, Kyungeun,Qiu, Lvyang Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.6

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform

Song, Wei,Feng, Ning,Tian, Yifei,Fong, Simon,Cho, Kyungeun Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.