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CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 834-842页

作者： Niu, Mengting Zhang, Jun Li, Yanjuan Wang, Cankun Liu, Zhaoqian Ding, Hui Zou, Quan Ma, Qin Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Heilongjiang Prov Land Reclamat Headquarters Gen Rehabil Dept Harbin Peoples R China Northeast Forestry Univ Sch Informat & Comp Engn Harbin 150040 Peoples R China Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Univ Elect Sci & Technol China Ctr Informat Biol Chengdu 610054 Peoples R China

Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://***./CirRNAPL/. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Circular RNA Extreme learning machine particle swarm optimization algorithm Identification Expression level

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Switchable Polarization Beam Splitter Based on GST-on-Silicon Waveguides

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2020年第2期12卷 1-10页

作者： Zhang, Bohao Chen, Weiwei Wang, Pengjun Dai, Shixun Jiang, Weifeng Liang, Wei Lu, Hao Ding, Jian Li, Jun Li, Yan Fu, Qiang Dai, Tingge Yu, Hui Yang, Jianyi Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Cyrus Tang Ctr Sensor Mat & Applicat Hangzhou 310027 Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Ningbo Univ Lab Infrared Mat & Devices Res Inst Adv Technol Ningbo 315211 Peoples R China

A switchable polarization beam splitter utilizing GST-on-silicon waveguides is introduced and comprehensively investigated in this paper. Based on the phase matching and mode hybridization, structural parameters of the presented device are optimized by using the particle swarm optimization algorithm and finite difference time domain method, so that the output fundamental transverse-electric (TE) and transverse-magnetic (TM) modes can be switched by controlling the phase state of GST and the device exhibits broad bandwidth, low crosstalk, and high polarization extinction ratio. For the designed device with the amorphous GST, the crosstalk is lower than dB and the polarization extinction ratio is larger than 21.35dB over the C-band. In the case of the crystalline GST, within the C-band, the crosstalk of less than 27.37dB and the polarization extinction ratio of greater than 21.37dB are achieved.

关键词： Switchable polarization beam splitter GST-on-silicon waveguide particle swarm optimization algorithm

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Application of improved genetic algorithm in ultrasonic location of transformer partial discharge

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NEURAL COMPUTING & APPLICATIONS 2020年第6期32卷 1755-1764页

作者： Zhu, Youchan Zhou, Li Xu, Haisheng N China Elect Power Univ Sch Control Comp Engn Baoding Peoples R China

Detecting and locating the partial discharge point inside the transformer is one of the key measures to ensure the normal operation of transformer. In this paper, an improved genetic algorithm (GAICM) based on improved cross-model was proposed. In this algorithm, crossover operator and mutation operator are improved, and an adaptive model is introduced. GAICM was tested by classical test functions. The test results showed that GAICM has good convergence speed and optimization ability. Meanwhile, GAICM was applied to the ultrasonic localization of transformer partial discharge, and its positioning effect was compared with the location effect of genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm. The experimental result showed that the application of GAICM to the ultrasonic localization of the transformer partial discharge is more effective.

关键词： Improved genetic algorithm particle swarm optimization algorithm Simulated annealing algorithm Transformer partial discharge

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Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 67-67页

作者： Li, Dujuan Chen, Caixia North Sichuan Med Coll Nanchong 631000 Peoples R China China West Normal Univ Nanchong 631000 Peoples R China

Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

关键词： Exercise fatigue Surface EMG signal Electrocardiogram signal Feature fusion particle swarm optimization algorithm

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Bi-level programming problem in the supply chain and its solution algorithm

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SOFT COMPUTING 2020年第4期24卷 2703-2714页

作者： Luo, Haiyan Liu, Linzhong Yang, Xun Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Econ & Management Lanzhou 730070 Peoples R China

Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where the upper level of a hierarchy may have his objective function and decision space partly determined by other levels. In addition, each planner's control instruments may allow him to influence the policies at other levels and thereby to improve his own objective function. As a tool, bi-level programming is applied for modeling decentralized decisions in which two decision makers make decisions successively. In this paper, we specifically address bi-level decision-making problems with budget constraint as an attractive feature in the context of enterprise-wide supply chain. We first describe the typical bi-level linear programming problem (BLLPP) and its optimal solution to the penalty function problem, and then, a cooperative decision-making problem in supply chain is modeled as BLLPP. A particle swarm optimization-based computational algorithm is designed to solve the problem, and the numerical example is presented to illustrate the proposed framework.

关键词： Decentralized supply chain Bi-level linear programming Budget constraint particle swarm optimization algorithm

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Shear strength prediction of short circular reinforced-concrete columns using soft computing methods

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ADVANCES IN STRUCTURAL ENGINEERING 2020年第14期23卷 3048-3061页

作者： Ketabdari, Hesam Karimi, Farzad Rasouli, Mahsa Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran *** Iran Qom Univ Fac Civil Engn Qom Iran

In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

关键词： ACI 318 ASCE-ACI 426 finite element model gene expression programming particle swarm optimization algorithm shear strength short circular reinforced-concrete columns

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Multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas/diesel fuel

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2020年第3期21卷 470-483页

作者： Ebrahimi, Mojtaba Najafi, Mohammad Jazayeri, Seyed Ali Islamic Azad Univ Dept Mech Engn Sci & Res Branch Tehran *** Iran KN Toosi Univ Technol Fac Mech Engn Tehran Iran

The aim of this study is to implement the multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas and diesel fuel. A single-cylinder heavy-duty diesel engine with a modified bathtub piston bowl profile is set on operation at 9.4 bar indicated mean effective pressure and running at a fixed engine speed of 1300 r/min. A certain amount of diesel fuel mass per cycle is fed into the engine at a fixed equivalence ratio without any exhaust gas recirculation. The optimization targets include reduction in engine emissions as much as possible, avoiding diesel knock occurrence, and achieving low temperature combustion concept with the least or no engine power losses. To implement the optimization, the effects of three control factors on the engine performance are assessed by the design of experiment concept-fractional factorial method. These selected control factors are intake temperature and intake pressure (both at intake valve closing) and the diesel fuel start of injection timing. Some randomized treatment combinations of chosen levels from the three selected control factors are employed to simulate reactivity-controlled compression-ignition combustion. Based on the engine's responses derived from the simulation, reactivity-controlled compression-ignition combustion's mathematical model is identified directly using an artificial neural network. Next, an optimization process is conducted using two different optimization algorithms, namely, genetic algorithm and particle swarm optimization algorithm. For assessing and validating the obtained optimal results, the obtained data are used to simulate reactivity-controlled compression-ignition combustion as the engine input factors. The results show that the proposed artificial neural network design is effectively capable of identifying reactivity-controlled compression-ignition combustion's mathematical model. Also, by optimizing reactivi

关键词： Reactivity-controlled compression ignition combustion diesel engine natural gas emission design of experiment factorial method artificial neural network multi-input-multi-output optimization genetic algorithm particle swarm optimization algorithm

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Bi-level programming model for resource-shared parking lots allocation

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2020年第7期12卷 501-511页

作者： Duan, Manzhen Wu, Dayong Liu, Hongchao North China Univ Sci & Technol Dept Civil & Architectural Engn Tangshan Peoples R China Texas Tech Univ Dept Civil Environm & Construct Engn Lubbock TX 79409 USA

The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. The upper-level of the model aimed to achieve the efficetive and balanced utilization of parking resonurces during peak time, while the lower-level model was to minimize the driver's walking distance after parking.A nested Particel swarm optimization algorithm was used to solve the proposed model. The simulation results of the model show that the peak congestion time has been reduced remarkably under the guidance of the proposed model. The Mean value of Unocuupied Parking Difference Index(MUPDI) curves trend to decline during the overall process. It means that within the acceptable walking distance, the proposed parking lots allocation model can effectively balance the utilization of parking resources shared in the service area and minimize walking distance as well.

关键词： Intelligent transportation system personalized parking guidance information system bi-level design particle swarm optimization algorithm parking lots allocation model

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A New Cycle Slip Detection and Repair Method Using a Single Receiver's Single Station B1 and L1 Frequencies in Ground-Based Positioning Systems

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SENSORS 2020年第2期20卷 346-346页

作者： Zhao, Xinyang Niu, Zun Li, Gaoxu Shuai, Qiangqiang Zhu, Bocheng Peking Univ Dept Elect Beijing 100871 Peoples R China

The detection and repair of the cycle slip is a key step for high precision navigation and positioning in indoor environments. Different methods have been developed to detect and repair cycle slips for carrier phase processing. However, most approaches are designed to eliminate the effects of the ionosphere in an outdoor environment, and many of them use pseudorange (code) information that is no longer suitable for indoor multipath environments. In this paper, a method based on the geometry-free combination without the pseudorange data is proposed to detect and fix cycle slips. A ground-based navigation system is built for data collection. Unlike the traditional dual-frequency cycle slip detection method, the Beidou B1, GPS L1 carrier phase combination is used instead of the B1, B2, or L1, L2 carrier phase combination, Ublox is used for data collecting. For fixing the cycle slips quickly, an improved adaptive particle swarm optimization (PSO) algorithm is employed. We compared the performance of the new method with the existing two methods using simulated data in different conditions. The results show that the proposed method has better performance than other methods.

关键词： cycle slip detection ground-based navigation multipath effect particle swarm optimization algorithm

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A two-dimensional quasi-optical system design method with low cross-polarization based on Gaussian beam mode analysis

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2020年第8期62卷 2728-2736页

作者： Chen, Tianyang Yao, Yuan Chen, Xiaodong Liu, Xiaoming Yu, Junsheng Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Queen Marry Univ London Sch Elect Engn & Comp Sci London England Anhui Normal Univ Sch Phys & Elect Informat Wuhu Anhui Peoples R China

In the design of quasi-optical systems, the cross-polarization introduced by off-axis mirrors is a major concern to us. By Gaussian beam mode analysis (GBMA), the electric field could be predicted precisely with this factor. In this study, we present some significant expressions and a method based on the energy of modes to analyze the influence of design parameters such as the distance between two objects and the incident angles of beams on the cross-polarization level of a two-dimensional (2D) multi-reflector system with a feed containing cross-polarization. Finally, combining these discussions, an innovative 2D quasi-optical system design algorithm with low cross-polarization based on the particle swarm optimization (PSO) algorithm is proposed considering various system variables. In order to verify the validity and accuracy of our method, we compare the results of our approach and physical optics (PO) in the commercial software package GRASP10 with the example of a double ellipsoidal mirror system.

关键词： cross-polarization Gaussian beam mode analysis Millimeter and submillimeter wave particle swarm optimization algorithm quasi-optical systems

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