A Path Planning Method for Unmanned Surface Vessels in Dynamic Environment

Jiabin Yu,Zhihao Chen,Zhiyao Zhao,Jiping Xu,Yang Lu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.4

A path planning method for unmanned surface vessels (USV) in dynamic environment is proposed to address the impact of dynamic environments on path planning results and the lack of dynamic obstacle avoidance capabilities. First, the considering ocean current rapidly exploring random tree (RRT*) (COC-RRT*) algorithm was proposed for global path planning. The RRT* algorithm has been enhanced with the integration of the virtual field sampling algorithm and ocean current constraint algorithm. The COC-RRT* algorithm optimizes the global planning path by adjusting the path between the parent nodes and child nodes. Second, according to the limitations of the International Regulations for Preventing Collisions at Sea (COLREGs), the improved dynamic window approach (DWA) is applied for local path planning. To enhance the ability of avoid dynamic obstacles, the dist function in the DWA algorithm has been improved. Simulation experiments were conducted in three scenarios to validate the proposed algorithm. The experimental results demonstrate that, in comparison with other algorithms, the proposed algorithm effectively avoids dynamic obstacles and mitigates the influence of the space-varying ocean current environment on the path-planning outcome. Additionally, the proposed algorithm exhibits high efficiency and robustness. The results verified the effectiveness of the proposed algorithm in dynamic environments.

Smooth Path Planning Method for Unmanned Surface Vessels Considering Environmental Disturbance

Jiabin Yu,Zhihao Chen,Zhiyao Zhao,Xiaoyi Wang,Yuting Bai,Jiguang Wu,Jiping Xu 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.10

To solve the problems of unsmooth path planning, insufficient dynamic obstacle avoidance ability, and environmental disturbance effect on the path planning result, this paper proposes a smooth path planning method for unmanned surface vessels (USVs) considering environmental disturbance. First, an improved A* algorithm, which uses the path smoothing method based on the minimum turning radius of a USV, is proposed for global path planning. The binary tree method is used instead of the enumeration method to select a relatively optimal path in the current situation to improve algorithm efficiency. In addition, the dynamic window approach (DWA) with the Convention on the International Regulation for Preventing Collision at Sea (COLREGs) constraints is used for local path planning. The dist function in the DWA algorithm is improved to enhance the DWA algorithm’s ability to avoid dynamic obstacles. Finally, the environmental disturbance function is derived and added to the A* and DWA algorithms to handle the effect of environmental disturbances, such as water flow, on the path planning result, which can significantly improve the path-planning ability of the algorithm in the presence of environmental disturbances. Simulation experiments are performed in three scenarios to verify the proposed algorithm. The experimental results show that compared with the other algorithms, the proposed algorithm can effectively avoid dynamic obstacles and reduce the impact of environmental disturbance on the path planning result. At the same time, the proposed algorithm has high efficiency and strong robustness.

Neural Network Based Adaptive Fuzzy PID-type Sliding Mode Attitude Control for a Reentry Vehicle

Zhen Jin,Jiabin Chen,Yongzhi Sheng,Xiangdong Liu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.1

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and externaldisturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilizationof radial basis function (RBF) neural network. In order to improve the transient performance and ensure smallsteady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by usingadaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loopsystem is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term ofsliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of apriori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbanceinformation. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numericalsimulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving thetransient performance and robustness.

A longitudinal analysis with CA-125 to predict overall survival in patients with ovarian cancer

An Jen Chiang,Jiabin Chen,Yu-Che Chung,Huan-Jung Huang,Wen Shiung Liou,Chung Chang 대한부인종양학회 2014 Journal of Gynecologic Oncology Vol.25 No.1

Objective: The objective of this study was to explore the association of longitudinal CA-125 measurements with overall survival (OS) time by developing a flexible model for patient-specific CA-125 profiles, and to provide a simple and reliable prediction of OS. Methods: A retrospective study was performed on 275 patients with ovarian cancer who underwent at least one cycle of primary chemotherapy in our institute. Serial measurements of patients’ CA-125 levels were performed at different frequencies according to their clinical plans. A statistical model coupling the Cox proportional hazards and the mixed-effects models was applied to determine the association of OS with patient-specific longitudinal CA-125 values. Stage and residual tumor size were additional variables included in the analysis. Results: A total of 1,601 values of CA-125 were included. Longitudinal CA-125 levels, stage, and the residual tumor size were all significantly associated with OS. A patient-specific survival probability could be calculated. Validation showed that, in average, 85.4% patients were correctly predicted to have a high or low risk of death at a given time point. Comparison with a traditional model using CA-125 half-life and time to reach CA-125 nadir showed that the longitudinal CA-125 model had an improved predicative value. Conclusion: Longitudinal CA-125 values, measured from the diagnosis of ovarian cancer to the completion of primary chemotherapy, could be used to reliably predict OS after adjusting for the stage and residual tumor disease. This model could be potentially useful in clinical counseling of patients with ovarian cancer.

Heat and mass transfer enhancement of the bubble absorption for a binary nanofluid

Xuehu Ma,Fengmin Su,Jiabin Chen,Yong Zhang 대한기계학회 2007 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.21 No.11

The main objective of this study is to enhance the heat and transfer process of absorption using the nanofluids as the working medium. Carbon nanotubes-ammonia nanofluids (the binary nanofluids) are prepared. The thermal conductivity of the binary nanofluids and the bubble absorption process enhancement are examined experimentally. The results show the thermal conductivity of the carbon nanotubes-ammonia nanofluid is higher 16% than that of NH₃/H₂O solution. And the carbon nanotubes-ammonia nanofluid has a great enhanced effect to the NH₃/H₂O absorption.

Simulation and experiment study of burrs in micro-milling Zr-based metallic glass

Jiachun Wang,Zhenhong Zhang,Chuang Zhang,Jiabin Fu,Jianchao Chen 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.7

Metallic glass has been widely used in making micro parts and equipment due to its excellent physical and chemical performance. A large quantity of burrs is produced in the micro-milling process that is hard to remove and seriously affects the quality and precision of the parts. Burrs should be effectively restrained; however, the burrs’ type, position and the effect of milling parameters on burrs’ generation in micro-milling metallic glass have not been systematically studied. In this paper, by using 3-D FEM simulation and taking micro-milling experiments of Zr-based metallic glass (Vit1), the burrs in flat-end milling and ball-end milling micro grooves were investigated. The burrs’ type and position were observed and summarized, the formation process of various burrs was analyzed in detail, and the influence of cutting parameters on burrs was clarified. Comparing the simulation and experiment result, we could confirm that the top burr and the entrance burr were produced during the processing of the flatend milling cutter, and ball-end milling cutter effectively inhibits the production of the top burr but takes no actions on burrs’ generation at the entrance and the bottom of the groove. The main cause of the top burr is the extrusion of the tool, and the extrusion of the cutting layer metal and chip accumulation were the main reason for entrance and exit burrs. Reducing the axial cutting depth could effectively restrain the generation of burrs for both two kinds of milling tools.

Data-Driven Cutting Parameters Optimization Method in Multiple Configurations Machining Process for Energy Consumption and Production Time Saving

Xikun Zhao,Congbo Li,Xingzheng Chen,Jiabin Cui,Bao Cao 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.3

Cutting parameters and machining configurations affect the energy consumption and production time in the machining process significantly. Previous cutting parameters optimization methods are proposed for a specific machining configuration that limits its generalization ability. However, the machining configuration varies constantly with actual machining tasks, which results in the predetermined optimization method is impractical. We propose a data-driven optimization method for the multiple machining configurations, aimed at reducing energy consumption and production time. Firstly, the analysis of the relationship between energy consumption and meta-actions under different machining states is carried out, and the Gaussian process regression (GPR)-based energy consumption model is proposed. Then, a multi-objective optimization model is proposed for energy consumption and production time reduction, which is solved via a multi-objective grey wolf optimization. Finally, the experiments are conducted to verify the validity of the proposed method and the influence of metaactions on energy consumption and production time are explicitly analyzed. The case study indicates the proposed energy consumption model has better prediction accuracy for multiple machining configurations. Optimizing cutting parameters achieves a trade-off between energy consumption and production time. Moreover, the parametric influence indicates cutting speed is the most influential cutting parameter for energy consumption and production time.

Autogenous Shrinkage and Crack Resistance of Carbon Nanotubes Reinforced Cement‑Based Materials

Yanming Liu,Tao Shi,Yujing Zhao,Yuan Gu,Zhifang Zhao,Jiabin Chen,Bingmiao Zheng,Shichong Shi 한국콘크리트학회 2020 International Journal of Concrete Structures and M Vol.14 No.5

Cracking caused by shrinkage deformation of cement-based materials at early age is a major problem leading to material failure in restrained conditions. Carbon nanotubes (CNTs) are incorporated into cement-based materials, and the autogenous shrinkage and crack resistance of the new composite materials obtained by linear shrinkage and ring tests are studied to solve the destruction of the materials caused by the shrinkage of cement-based materials. The results showed that addition of CNTs significantly inhibited the autogenous shrinkage of cement-based materials with maximum reduction rate above 40%. CNTs also significantly improved the cracking resistance of cement-based materials. The optimal effect was noticed at CNTs content of 0.1 wt%. The incorporation of CNTs not only inhibits the autogenous shrinkage of cement-based materials, but also inhibits the drying shrinkage of cement-based materials to some extent. Therefore, carbon nanotubes have the potential to solve the destruction of materials caused by shrinkage of cement-based materials.

Bacteria-based multiplex system eradicates recurrent infections with drug-resistant bacteria via photothermal killing and protective immunity elicitation

Youcui Xu,Yi Wu,Yi Hu,Mengran Xu,Yanyan Liu,Yuting Ding,Jing Chen,Xiaowan Huang,Longping Wen,Jiabin Li,Chen Zhu 한국생체재료학회 2023 생체재료학회지 Vol.27 No.00

Background The high mortality associated with drug-resistant bacterial infections is an intractable clinical problem resulting from the low susceptibility of these bacteria to antibiotics and the high incidence of recurrent infections. Methods Herein, a photosynthetic bacteria-based multiplex system (Rp@Al) composed of natural Rhodopseudomonas palustris (Rp) and Food and Drug Administration-approved aluminum (Al) adjuvant, was developed to combat drug-resistant bacterial infections and prevent their recurrence. We examined its photothermal performance and in vitro and in vivo antibacterial ability; revealed its protective immunomodulatory effect; verified its preventative effect on recurrent infections; and demonstrated the system’s safety. Results Rp@Al exhibits excellent photothermal properties with an effective elimination of methicillin-resistant Staphylococcus aureus (MRSA). In addition, Rp@Al enhances dendritic cell activation and further triggers a T helper 1 ( TH1)/TH2 immune response, resulting in pathogen-specific immunological memory against recurrent MRSA infection. Upon second infection, Rp@Al-treated mice show significantly lower bacterial burden, faster abscess recovery, and higher survival under near-lethal infection doses than control mice. Conclusions This innovative multiplex system, with superior photothermal and immunomodulatory effects, presents great potential for the treatment and prevention of drug-resistant bacterial infections.

Risk factors in progression from endometriosis to ovarian cancer: a cohort study based on medical insurance data

An Jen Chiang,Chung Chang,Chi-Hsiang Huang,Wei-Chun Huang,,Yuen-Yee Kan,Jiabin Chen 대한부인종양학회 2018 Journal of Gynecologic Oncology Vol.29 No.3

Objective: The objective was to identify risk factors that were associated with the progression from endometriosis to ovarian cancer based on medical insurance data. Methods: The study was performed on a dataset obtained from the National Health Insurance Research Database, which covered all the inpatient claim data from 2000 to 2013 in Taiwan. The International Classification of Diseases (ICD) code 617 was used to screen the dataset for the patients who were admitted to hospital due to endometriosis. They were then tracked for subsequent diagnosis of ovarian cancer, and available biological, socioeconomic and clinical information was also collected. Univariate and multivariate analyses were then performed based on the Cox regression model to identify risk factors. C-index was calculated and cross validated. Results: A total of 229,617 patients who were admitted to hospital due to endometriosis from 2000 to 2013 were included in the study, out of whom 1,473 developed ovarian cancer by the end of 2013. A variety of factors, including age, residence, hospital stratification, premium range, and various comorbidities had significant impact on the progression (p<0.05). Among them, age, urbanization of residence, hospital stratification, premium range, post-endometriosis childbearing, pelvic inflammation, and depression all had independent, significant impact (p<0.05). The validated C-index was 0.69. Conclusion: For a woman diagnosed with endometriosis, increased age, residing in a highly urbanized area, low or high income, depression, pelvic inflammation, and absence of childbearing post-endometriosis all put her at high-risk to develop ovarian cancer. The findings may be of help to gynecologists to identify high-risk patients.