Qian Xiao

Qian Xiao

Tianjin University

H-index: 15

Asia-China

About Qian Xiao

Qian Xiao, With an exceptional h-index of 15 and a recent h-index of 15 (since 2020), a distinguished researcher at Tianjin University, specializes in the field of Microgrids, DC Distribution Network, Multilevel Converters, BESS, Energy Router.

His recent articles reflect a diverse array of research interests and contributions to the field:

Review of fault diagnosis and fault-tolerant control methods of the modular multilevel converter under submodule failure

Synthetic thermal convolutional‐memory network for the lithium‐ion battery behaviour diagnosis against noise interruptions

Submodule Capacitance Monitoring Approach for the MMC With Asymptotically Converged Error

Novel modular multilevel converter-based five-terminal MV/LV hybrid AC/DC microgrids with improved operation capability under unbalanced power distribution

Improved LightGBM-based framework for electric vehicle lithium-ion battery remaining useful life prediction using multi health indicators

Reinforced Multi-scale Data-model Alliance Network (RMS-DMAN): a Real-time Multi-step Thermal Warning of Energy Storage System

Distributed OPF for PET-based AC/DC distribution networks with convex relaxation and linear approximation

STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety

Qian Xiao Information

University

Tianjin University

Position

Assitant Professor

Citations(all)

844

Citations(since 2020)

831

Cited By

152

hIndex(all)

15

hIndex(since 2020)

15

i10Index(all)

21

i10Index(since 2020)

21

Email

University Profile Page

Tianjin University

Qian Xiao Skills & Research Interests

Microgrids

DC Distribution Network

Multilevel Converters

BESS

Energy Router

Top articles of Qian Xiao

Review of fault diagnosis and fault-tolerant control methods of the modular multilevel converter under submodule failure

Authors

Qian Xiao,Yu Jin,Hongjie Jia,Yi Tang,Allan Fagner Cupertino,Yunfei Mu,Remus Teodorescu,Frede Blaabjerg,Josep Pou

Journal

IEEE Transactions on Power Electronics

Published Date

2023/6/6

Modular multilevel converters (MMCs) have attracted extensive research interests in various ac and dc conversion applications due to their modular structure and excellent harmonic performance. However, the large number of power switches increases the potential risk of submodule (SM) failure, which greatly challenges the safe and reliable operation of the MMC. This article presents a detailed review of fault diagnosis and fault-tolerant control methods of the MMC under SM failures. On this basis, comprehensive comparisons are conducted among different fault diagnosis methods, and verification results are provided to analyze the advantages and disadvantages of the popular fault-tolerant control methods. Finally, the review is concluded, and future trends and research opportunities are discussed.

Synthetic thermal convolutional‐memory network for the lithium‐ion battery behaviour diagnosis against noise interruptions

Authors

Marui Li,Chaoyu Dong,Rui Wang,Xiaodan Yu,Qian Xiao,Hongjie Jia

Journal

IET Energy Systems Integration

Published Date

2023/3

In order to meet the two global challenges of energy shortage and environmental pollution, various countries have begun to advocate the application of new energy equipment such as electric vehicles. This has also promoted the development of energy storage equipment and energy storage systems. With their high performance, lithium‐ion batteries are used in a wide range of electrical equipment. But the safety of lithium‐ion batteries depends on effective behaviour diagnosis. In order to better realise behaviour diagnosis, this paper combined the long and short‐term memory network (LSTM) with the temporal convolution network (TCN) for the first time and established a synthetic thermal convolutional‐memory network (STCMN) for lithium‐ion battery behaviour diagnosis against noise interruptions. In addition, a TCN‐LSTM alliance network structure is designed. The TCN‐LSTM alliance network is an effective …

Submodule Capacitance Monitoring Approach for the MMC With Asymptotically Converged Error

Authors

Qian Xiao,Huai Wang,Yu Jin,Hongjie Jia,Yunfei Mu,Jiebei Zhu,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE Transactions on Industrial Electronics

Published Date

2023/6/26

To reduce noise interferences and improve the steady-state estimation accuracy of submodule (SM) capacitors, a novel SM capacitance monitoring approach has been proposed for the modular multilevel converter in this article. First, the fundamental frequency (FF) component is analyzed to be dominant of the capacitor voltage squared. Then, based on the energy equation W = Cv 2 /2, half of the FF capacitor voltage squared is selected as the input variable, and the FF capacitor energy calculated by power integral is selected as the feedback component. By proper design of the estimation law, closed-loop capacitance monitoring can be achieved with reduced noise interference. Furthermore, based on the Lyapunov stability criterion, the estimation parameter is designed so that the estimation error asymptotically converges to zero. Compared with the conventional methods, the proposed approach is easy to …

Novel modular multilevel converter-based five-terminal MV/LV hybrid AC/DC microgrids with improved operation capability under unbalanced power distribution

Authors

Qian Xiao,Yunfei Mu,Hongjie Jia,Yu Jin,Xiaodan Yu,Remus Teodorescu,Josep M Guerrero

Journal

Applied Energy

Published Date

2022/1/15

Conventionally, the multilevel converter-based multi-terminal hybrid microgrids require a large number of power switches and have a limited operation capability under unbalanced power distribution in medium and low voltage (MV/LV) AC/DC microgrids. To solve this issue, this paper proposes the novel modular multilevel converter (MMC)-based five-terminal MV/LV hybrid AC/DC microgrids. The proposed hybrid microgrids realize the interconnection between the medium-voltage AC (MVAC), MVDC, low voltage AC (LVAC), and two LVDC terminals. In addition, the MVAC grid is connected to the AC terminal of MMC, and the MVDC microgrid is connected to the DC terminal of MMC through a dual active bridge (DAB) converter. Based on MMC, the compact interlinking converters are established, providing three LVDC terminals, which are connected to two LVDC microgrids and one LVAC microgrid through a DC …

Improved LightGBM-based framework for electric vehicle lithium-ion battery remaining useful life prediction using multi health indicators

Authors

Huiqiao Liu,Qian Xiao,Yu Jin,Yunfei Mu,Jinhao Meng,Tianyu Zhang,Hongjie Jia,Remus Teodorescu

Journal

Symmetry

Published Date

2022/8/1

To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the …

Reinforced Multi-scale Data-model Alliance Network (RMS-DMAN): a Real-time Multi-step Thermal Warning of Energy Storage System

Authors

Marui Li,Chaoyu Dong,Yunfei Mu,Qian Xiao,Hongjie Jia

Published Date

2022/7/17

Battery energy storage system, especially with the increasing popularity of renewable energy, has been endowed with a more imperative role. However, high temperature will accelerate the side reaction of the battery, and affect its safety and performance. To address this issue, it is imperative to assess the temperature performance of the energy storage system. At present, existing approaches are either based on the electrothermal model or data-driven algorithm, which ignores the inherent relationship between the physical model and operational data. In this paper, a data-model alliance module is established, which combines model-based method and data-based method. With four components of hybrid heating prediction module, multi-scale temperature prediction module, data-model alliance module, and multi-step ahead thermal warning module, a reinforced multi-scale data-model alliance network (RMS …

Distributed OPF for PET-based AC/DC distribution networks with convex relaxation and linear approximation

Authors

Tao Zhang,Yunfei Mu,Junbo Zhao,Hongjie Jia,Qian Xiao

Journal

IEEE Transactions on Smart Grid

Published Date

2022/5/30

A power electronic transformer (PET) can flexibly regulate the power flow of a network and facilitate the integration of distributed energy resources via AC/DC networks. With the continuous expansion of the scale of multiregional interconnection networks, the traditional centralized optimization method faces great challenges. This paper proposes a distributed convex optimal power flow (OPF) model by leveraging the hierarchical structural characteristics of PET enabled AC/DC distribution networks. An analytical target cascading method is introduced to divide the centralized OPF problem into several subproblems that can make the subnetworks operate individually and coordinate with each other. The PET flexibility is also harnessed to reduce network power loss and voltage deviation. To improve the computational efficiency and algorithm convergence, the convex relaxation and linear approximation methods are …

STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety

Authors

Marui Li,Chaoyu Dong,Binyu Xiong,Yunfei Mu,Xiaodan Yu,Qian Xiao,Hongjie Jia

Journal

Applied Energy

Published Date

2022/12/15

The internal reactions of lithium-ion batteries are susceptible to temperature, which makes the temperature of significant impact on their safety and performance. Therefore, it is very important to predict the temperature trend of lithium-ion batteries and implement thermal early warning. In order to solve this thermal concern of lithium-ion batteries, this paper designed a sequential-transformer thermal early warning system (STTEWS). First, a new allied temporal convolution-recurrent diagnosis network (TCRDN) is constructed by combining LSTM and temporal convolution network (TCN) using an adaptive boosting algorithm. Then, a complete transformer thermal diagnosis network (TTDN) is established, which fuses the important information from lithium-ion battery thermal images and integrates the prediction results from TCRDN to achieve an accurate early warning function. TTDN combines state-of-the-art time series …

Multi-agent schedule optimization method for regional energy internet considering the improved tiered reward and punishment carbon trading model

Authors

Tianxiang Li,Qian Xiao,Hongjie Jia,Yunfei Mu,Xinying Wang,Wenbiao Lu,Tianjiao Pu

Journal

Frontiers in Energy Research

Published Date

2022/5/24

Regional energy internet (REI) contains massive market agents, whose interests and objectives vary from each other. In consequence, it is challenging to stimulate the energy conservation and emissions reduction participation of each agent by the conventional schedule optimization method. This paper proposes a multi-agent schedule optimization method for REI considering the improved tiered reward and punishment carbon trading model. Firstly, the energy flow constraints and device constraints of REI are established. Secondly, to tighten restrictions on carbon emissions, the relative carbon emission is used as the criterion to establish the improved tied reward and punishment carbon trading model. Next, to analyze the real multi-agent game situation in the market, different agents are classified, and the objective functions are defined based on their revenue. Finally, a two-layer algorithm is used to solve the above multi-agent model. Simulation results verify that: the proposed method can significantly reduce carbon emissions and enhance the revenue of the region.

Dual-layer modulated model predictive control scheme for the cascaded h-bridge converter

Authors

Qian Xiao,Hongjie Jia,Yi Tang,Yu Jin,Yunfei Mu,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE Transactions on Industrial Electronics

Published Date

2022/12/1

To simultaneously achieve fast dynamics and fixed switching frequency, a dual-layer modulated model predictive control scheme is proposed for the cascaded H-bridge converter in this article. For the output current control layer, first, each phase cluster is considered as a whole, and only six nonzero vectors are evaluated for output current prediction. Then, two optimal vectors are selected, and their dwell times are calculated based on the current prediction results. As a result, fast output current dynamics can be obtained with a greatly reduced number of evaluated control options. For the cluster voltage balancing control layer, instead of fundamental-frequency zero-sequence voltage (ZSV), the adaptive ZSV is injected according to the predicted cluster voltage differences. As a result, the active power flows are adjusted in each control period, and the cluster voltage balancing speed can be significantly improved. By …

A Dual-Loop Control Strategy for Interlinking Converters in Hybrid AC/DC Microgrids

Authors

Yuwei Zhang,Qian Xiao,Zhipeng Jiao,Wenbiao Lu,Jin Xu,Yunfei Mu,Hongjie Jia

Published Date

2022/4/17

A dual-loop control strategy for interlinking converters (ICs) has been proposed in this paper. Firstly, in the microgrids (MGs), droop control provides frequency and voltage reference. Then, for IC, a dual-loop control structure is adopted. The outer loop is based on the droop characteristic to achieve the control objective; the inner loop is based on the idea of constant power control to achieve the tracking without static error of the power reference value. This strategy reduces the communications effectively, and the IC only operates in two modes that operation mode and power distribution mode are optimized. The control structure is simple and extensible. Finally, the hybrid AC/DC MG system has been built in MATLAB/Simulink, and the simulation results verify the effectiveness of the control strategy.

Simplified Carrier-Based Space Vector Modulation Scheme for the Modular Multilevel Converter

Authors

Weiliang Wang,Qian Xiao,Huiqiao Liu,Yu Jin,Yunfei Mu,Hongjie Jia

Published Date

2022/10/28

To improve the dc voltage utilization rate, this paper proposes a simplified carrier-based space vector modulation (SVM) scheme for the modular multilevel converter (MMC). Firstly, by considering each arm as a whole, only eight space vectors are evaluated in each control period. As a result, the vector selection and dwell time calculation process can be simplified. Then, by flexible selection of extra zero vectors in the MMC, the common-mode voltage (CMV) can be suppressed. Finally, phase-shifted carriers are applied to generate multilevel output voltage waveforms. Experimental results show that the proposed SVM scheme can be achieved with suppressed CMV under different modulation indexes, even in overmodulation conditions.

An enhanced data-driven model for lithium-ion battery state-of-health estimation with optimized features and prior knowledge

Authors

Huanyang Huang,Jinhao Meng,Yuhong Wang,Lei Cai,Jichang Peng,Ji Wu,Qian Xiao,Tianqi Liu,Remus Teodorescu

Journal

Automotive Innovation

Published Date

2022/4

In the long-term prediction of battery degradation, the data-driven method has great potential with historical data recorded by the battery management system. This paper proposes an enhanced data-driven model for Lithium-ion (Li-ion) battery state of health (SOH) estimation with a superior modeling procedure and optimized features. The Gaussian process regression (GPR) method is adopted to establish the data-driven estimator, which enables Li-ion battery SOH estimation with the uncertainty level. A novel kernel function, with the prior knowledge of Li-ion battery degradation, is then introduced to improve the modeling capability of the GPR. As for the features, a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency. In the first stage, an optimal partial charging voltage is selected by the grid search; while in the second stage, the principal component …

Fine-grained Dynamics Representation and Stability Analysis for MMC-based Hybrid AC/DC Power Systems

Authors

Jingming Cao,Chaoyu Dong,Qian Xiao,Marui Li,Xiaodan Yu,Hongjie Jia

Published Date

2022/9/5

The hybrid AC/DC power system is favored because of its huge energy transmission capacity and excellent steerability. However, the real system is large in scale and the inner dynamics interaction and coupling are intricate, which introduces a series of stability issues for the system operation. To investigate AC/DC interaction dynamics, an MMC-based hybrid power system model is established in the state space. The model considers the detailed dynamics of both AC and DC, and their interaction, thus it can be used for AC/DC interaction study, and offer a more precise stability assessment result than either AC power system model or MMC converter model. After the verification in MATLAB/Simulink, The interaction is studied thoroughly by the proposed model, which clearly identifies the harmonic coupling between MMC and traditional power systems. The small signal stability gaps of different systems are also …

A novel detection and localization approach of open-circuit switch fault for the grid-connected modular multilevel converter

Authors

Yu Jin,Qian Xiao,Hongjie Jia,Yanchao Ji,Tomislav Dragičević,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE Transactions on Industrial Electronics

Published Date

2022/3/1

The open-circuit fault detection and localization (FDL) technique can improve the reliability of the modular multilevel converter (MMC). However, the conventional software-based FDL methods usually have a heavy computation burden or a limited localization speed. This article proposes a simplified and fast software-based FDL approach for the grid-connected MMC. First, the errors between the measured state variables (the output current and the circulating current) and their estimated values are calculated. By comparing these errors with their threshold values, the switch fault can not only be detected but also be localized to the specific arm. Then, the capacitor voltages in this faulty arm are collected, and the submodule (SM) with the highest capacitor voltage is selected. To confirm the switch fault in this SM, a modified Pauta criterion is presented to check the abnormal voltage data. As a result, the computation …

SNNFT: Sequential Neural Network-Fuzzy Thermal Early Warning System for Lithium-ion Batteries

Authors

Marui Li,Chaoyu Dong,Yunfei Mu,Qian Xiao,Jingming Cao,Hongjie Jia

Published Date

2022/9/5

Due to the promotion of electric vehicles and new energy sources, lithium-ion batteries have been widely used. However, temperature has a great influence on the performance and safety of lithium-ion batteries during operation. Therefore, it is very important to predict the temperature of lithium-ion batteries and implement thermal early warning. In order to solve this problem, this paper designed a Sequential neural network-fuzzy thermal early warning system (SNNFT). First, the SNNFT uses a denoising autoencoder to eliminate the noise in real-time measurement. Then it combines the long short-term memory net-work and the temporal convolutional network that can handle the time series problem well to realize the accurate prediction of the lithium-ion battery temperature. And the SNNFT applies interpretable adaptive network-based fuzzy inference system model to build thermal early warning system. Complete …

Decoupled control scheme for THD reduction and one specific harmonic elimination in the modular multilevel converter

Authors

Qian Xiao,Shunfeng Yang,Yu Jin,Hongjie Jia,Josep Pou,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE Transactions on Industrial Electronics

Published Date

2022/3/1

This article proposes a decoupled control scheme for the modular multilevel converter (MMC) to reduce the total harmonic distortion (THD) and eliminate one specific harmonic. First, to realize the decoupled control between the ac and dc paths of the MMC, an improved nearest level control (INLC) method is proposed. It applies the round function to generate the ac output voltage levels, instead of the arm output voltage levels. Thus, a staircase wave is generated by the INLC method. Then, by adding an additional pulse to each quarter of the staircase wave, one specific harmonic can be eliminated without optimizing each conduction angle. Furthermore, a decoupled circulating current fuzzy control method is proposed to suppress the second-order harmonic and balance the arm energy. By this decoupled control structure, the output voltage and the circulating current can be controlled independently. Taking the fifth …

An improved energy management for MVDC distribution system based on exponential droop control

Authors

Wenbiao Lu,Qian Xiao,Hongjie Jia,Yunfei Mu,Mingjian Cui,Yu Jin,Tianxiang Li,Yuwei Zhang,Xiaodan Yu

Journal

Frontiers in Energy Research

Published Date

2022/9/1

The energy management optimization and DC voltage stability are the main challenges when loads surging and renewable energy sources (RESs) fluctuations occur in the medium-voltage DC distribution system (MVDC-DS). To solve these issues, this paper proposes a dual-time scale energy management method based on improved droop control for the MVDC-DS. Firstly, a ±10kV three-terminal hybrid modular multilevel converter (MMC)-based ring MVDC-DS with DC fault ride-through capability is constructed, which including electric vehicle charging stations, energy storage stations, multiple loads and RESs. Secondly, the droop control adopted by the hybrid MMC is improved by exp-function to achieve higher power quality. On this basis, a dual-time scale energy management method is proposed to minimize the electricity purchasing cost. The reference powers of each controllable unit are optimized in the long time-scale, and the parameters of improved droop control are optimized in the short time-scale. Finally, a case study is conducted on the ±10kV three-terminal hybrid MMC-based ring MVDC-DS, and the results indicate that the proposed method can improve the economy and power quality of system, and the exp-droop control can maintain the normal system operation under different conditions.

A novel fault-tolerant operation approach for the modular multilevel converter-based STATCOM with the enhanced operation capability

Authors

Yu Jin,Qian Xiao,Josep Pou,Hongjie Jia,Yanchao Ji,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE Journal of Emerging and Selected Topics in Power Electronics

Published Date

2022/2/15

The operation capability of the modular multilevel converter (MMC)-based static synchronous compensator (STATCOM) is limited under submodule (SM) failures. When running in the STATCOM mode, the floating dc-link voltage of the MMC is adjustable. Therefore, this article proposes a novel fault-tolerant operation approach to enhance the operation capability of the MMC under severe SM failures. First, the dc-link voltage is dynamically regulated under SM failures, where the capacitor voltages of all SMs are slightly increased. The modulation ranges of the faulty and healthy phases can be enlarged, and the even voltage stress distribution can be realized. Then, a low-magnitude fundamental frequency zero-sequence voltage (FF-ZSV) is injected to balance the line-to-line voltages. Finally, the virtual energy idea is proposed, where the capacitor voltages of faulty SMs are considered to be clamped at the reference …

Space-vector-equalized predictive current control scheme for the modular multilevel converter with improved steady-state performance

Authors

Qian Xiao,Yu Jin,Josep Pou,Hongjie Jia,Yunfei Mu,Remus Teodorescu,Frede Blaabjerg

Journal

IEEE transactions on industrial electronics

Published Date

2022/8/30

A large number of control options and steady-state tracking errors are two main challenges for model predictive control in the modular multilevel converter (MMC). To solve these issues, each arm of the three-phase MMC is considered as a whole, and a space-vector-equalized predictive current control scheme is proposed for the three-phase MMC in this article. First, eight equalized space vectors are involved, and only six nonzero vectors are evaluated in each period. As a result, the number of evaluated control options can be significantly reduced. Then, two optimal vectors are selected, and their dwell times are calculated based on the predicted output currents of the MMC. Furthermore, the compensation terms are designed and added to the dwell times so that the steady-state performance can be improved by the proposed scheme. Experimental results show that the proposed scheme provides fast dynamic …

See List of Professors in Qian Xiao University(Tianjin University)

Qian Xiao FAQs

What is Qian Xiao's h-index at Tianjin University?

The h-index of Qian Xiao has been 15 since 2020 and 15 in total.

What are Qian Xiao's top articles?

The articles with the titles of

Review of fault diagnosis and fault-tolerant control methods of the modular multilevel converter under submodule failure

Synthetic thermal convolutional‐memory network for the lithium‐ion battery behaviour diagnosis against noise interruptions

Submodule Capacitance Monitoring Approach for the MMC With Asymptotically Converged Error

Novel modular multilevel converter-based five-terminal MV/LV hybrid AC/DC microgrids with improved operation capability under unbalanced power distribution

Improved LightGBM-based framework for electric vehicle lithium-ion battery remaining useful life prediction using multi health indicators

Reinforced Multi-scale Data-model Alliance Network (RMS-DMAN): a Real-time Multi-step Thermal Warning of Energy Storage System

Distributed OPF for PET-based AC/DC distribution networks with convex relaxation and linear approximation

STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety

...

are the top articles of Qian Xiao at Tianjin University.

What are Qian Xiao's research interests?

The research interests of Qian Xiao are: Microgrids, DC Distribution Network, Multilevel Converters, BESS, Energy Router

What is Qian Xiao's total number of citations?

Qian Xiao has 844 citations in total.

What are the co-authors of Qian Xiao?

The co-authors of Qian Xiao are Frede Blaabjerg, Josep M. Guerrero, Remus Teodorescu, Peng Wang, Tomislav Dragičević, Josep Pou.

    Co-Authors

    H-index: 197
    Frede Blaabjerg

    Frede Blaabjerg

    Aalborg Universitet

    H-index: 147
    Josep M. Guerrero

    Josep M. Guerrero

    Aalborg Universitet

    H-index: 104
    Remus Teodorescu

    Remus Teodorescu

    Aalborg Universitet

    H-index: 83
    Peng Wang

    Peng Wang

    Nanyang Technological University

    H-index: 72
    Tomislav Dragičević

    Tomislav Dragičević

    Danmarks Tekniske Universitet

    H-index: 67
    Josep Pou

    Josep Pou

    Nanyang Technological University

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