Sodium Alginate-Assisted Synthesis of PdAg Bimetallic Nanoparticles and their Enhanced Activity for Electrooxidation of Ethanol

Yanru Yin,Changna Wen,Ning Ma,Baoyan Wang,Lianying Zhang,Hongliang Li,Peizhi Guo 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2019 NANO Vol.14 No.9

Palladium and palladium-silver bimetallic nanocrystals have been synthesized hydrothermally by using environmental-friendly sodium alginate as the stabilizer and reducing agent. The pure palladium nanoparticles were spherical-like possibly due to the principle of the lowest surface energy, however, the formation of bimetallic palladium-silver nanoparticles was much more complicated, which was thinner and more irregular nanostructures than pure palladium nanoparticles. Electrochemical measurements showed that the electrocatalytic activity toward ethanol oxidation was increased first with the increase of silver content in bimetallic nanoparticles, from pure palladium of around 1070 mA/mg, to PdAg-20 of 1160 mA/mg and to PdAg-10 of 1750 mA/mg, and declined greatly at a high content of silver, approximately 279 mA/mg. Electrochemical stability test showed that PdAg-10 and PdAg-5 were the best and worst among four palladium-based samples, respectively. Based on the experimental data, the formation mechanism of pure palladium and palladium-silver bimetallic nanoparticles and the structure-property relationship of these samples have been discussed.

DSLA: Dynamic Sampling Localization Algorithm Based on Virtual Anchor Node

( Yanru Chen ),( Bingshu Yan ),( Liangxiong Wei ),( Min Guo ),( Feng Yin ),( Qian Luo ),( Wei Wang ),( Liangyin Chen ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.10

Compared with the localization methods in the static sensor networks, node localization in dynamic sensor networks is more complicated due to the mobility of the nodes. Dynamic Sampling Localization Algorithm Based on Virtual Anchor (DSLA) is proposed in this paper to localize the unknown nodes in dynamic sensor networks. Firstly, DSLA algorithm predicts the speed and movement direction of nodes to determine a sector sampling area. Secondly, a method of calculating the sampling quantity with the size of the sampling area dynamically changing is proposed in this paper. Lastly, the virtual anchor node, i.e., the unknown node that got the preliminary possible area (PLA), assists the other unknown nodes to reduce their PLAs. The last PLA is regarded as a filtering condition to filter out the conflicting sample points quickly. In this way, the filtered sample is close to its real coordinates. The simulation results show that the DSLA algorithm can greatly improve the positioning performance when ensuring the execution time is shorter and the localization coverage rate is higher. The localization error of the DSLA algorithm can be dropped to about 20%.

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Yin, Zhonggang,Li, Guoyin,Du, Chao,Sun, Xiangdong,Liu, Jing,Zhong, Yanru The Korean Institute of Power Electronics 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.1

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Yin, Zhonggang,Gong, Lei,Du, Chao,Liu, Jing,Zhong, Yanru The Korean Institute of Power Electronics 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Zhonggang Yin,Guoyin Li,Chao Du,Xiangdong Sun,Jing Liu,Yanru Zhong 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.1

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Zhonggang Yin,Lei Gong,Chao Du,Jing Liu,Yanru Zhong 전력전자학회 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

On-line Identification Methods of Parameters for Permanent Magnet Synchronous Motors Based on Cascade MRAS

Yanqing Zhang,Zhonggang Yin,Xiangdong Sun,Yanru Zhong 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6

Motor parameters should be on-line estimated to realize precise control of PMSM in sensorless vector control system. In this paper, an on-line identification method for PMSM parameters based on cascade MRAS is proposed by analyzing the conventional MRAS. By means of Popov’s hyper-stability theory, the model of motor parameters identification is built in synchronous d-q coordinates, and PMSM stator voltage, stator current and their errors are used to obtain the adaptive laws of motor parameters, and it is realizable to estimate rotor speed, stator resistance and rotor flux at the same time. The simulation results demonstrate the correctness and effectiveness of the proposed method.

Associations of Genetic Variations in Mismatch Repair Genes MSH3 and PMS1 with Acute Adverse Events and Survival in Patients with Rectal Cancer Receiving Postoperative Chemoradiotherapy

Jie Yang,Ying Huang,Yanru Feng,Hongmin Li,Ting Feng,Jinna Chen,Luxi Yin,Weihu Wang,Shulian Wang,Yueping Liu,Yongwen Song,Yexiong Li,Jing Jin,Wen Tan,Dongxin Lin 대한암학회 2019 Cancer Research and Treatment Vol.51 No.3

Purpose Mismatch repair (MMR) deficiency plays a critical role in rectal cancer. This study aimed to explore the associations between genetic variations in seven MMR genes and adverse events (AEs) and survival of patients with rectal cancer treated with postoperative chemoradiotherapy (CRT). Materials and Methods Fifty single nucleotide polymorphisms in seven MMR (MLH1, MLH3, MSH2, MSH3, MSH6, PMS1 and PMS2) genes were genotyped by Sequenom MassARRAY method in 365 patients with locally advanced rectal cancer receiving postoperative CRT. The associations between genotypes and AEs were measured by odds ratios and 95% confidence intervals (CIs) by unconditional logistic regression model. The associations between genetic variations and survival were computed by the hazard ratios and 95% CIs by Cox proportional regression model. Results The most common grade ! 2 AEs in those 365 patients, in decreasing order, were diarrhea (44.1%), leukopenia (29.6%), and dermatitis (18.9%). Except 38 cases missing, 61 patients (18.7%) died during the follow-up period. We found MSH3 rs12513549, rs33013, and rs6151627 significantly associated with the risk of grade ! 2 diarrhea. PMS1 rs1233255 had an impact on the occurrence of grade ! 2 dermatitis. Meanwhile, PMS1 rs4920657, rs5743030, and rs5743100 were associated with overall survival time of rectal cancer. Conclusion These results suggest that MSH3 and PMS1 polymorphisms may play important roles in AEs prediction and prognosis of rectal cancer patients receiving postoperative CRT, which can be potential genetic biomarkers for rectal cancer personalized treatment.