BARS: Towards Open Benchmarking for Recommender Systems

The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using different experimental settings. Such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project (namely BARS) aiming for open benchmarking for recommender systems. In comparison to some earlier attempts towards this goal, we take a further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It covers both matching and ranking tasks, and also enables researchers to easily follow and contribute to the research in this field. This project will not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems. We would like to call upon everyone to use the BARS benchmark for future evaluation, and contribute to the project through the portal at: https://openbenchmark.github.io/BARS.Comment: Accepted by SIGIR 2022. Note that version v5 is updated to keep consistency with the ACM camera-ready versio

Cycle Representation Learning for Inductive Relation Prediction

In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based on the mathematics of algebraic topology, we propose a novel solution for inductive relation prediction, an important learning task for knowledge graph completion. To predict the relation between two entities, one can use the existence of rules, namely a sequence of relations. Previous works view rules as paths and primarily focus on the searching of paths between entities. The space of rules is huge, and one has to sacrifice either efficiency or accuracy. In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology. By exploring the linear structure of the cycle space, we can improve the searching efficiency of rules. We propose to collect cycle bases that span the space of cycles. We build a novel GNN framework on the collected cycles to learn the representations of cycles, and to predict the existence/non-existence of a relation. Our method achieves state-of-the-art performance on benchmarks.Comment: Accepted in ICML 202

Extended State Observer based Flatness Control for Fuel Cell Output Series Interleaved Boost Converter

International audienceOutput series interleaved Boost converter (OS-IBC) has become a promising candidate for fuel cell application, due to the features of high voltage gain and low input current ripple. To achieve a satisfactory control performance for the OS-IBC, a robust dual loop control strategy is designed in this paper. The designed controller comprises an inner loop dedicated to inductor current tracking and an outer loop aimed at output voltage regulation, which both based on flatness control. Furthermore, the extended state observers (ESO) are integrated into this control algorithm for online estimating the uncertain input voltage and output current of the equivalent converter. With this ESO, the parasitic circuit parameters are considered into the estimations, and meanwhile the input source voltage sensor and load current sensor can be omitted. The feasibility and robustness of the designed controller have been successfully validated by simulation and experimental results obtained using a 240W prototype converter and a dSPACE control platform

A Novel Nonisolated Multi-port Bidirectional DC-DC Converter With High Voltage Gain for Fuel Cell Hybrid System

International audienceIn this paper, a novel multi-port bidirectional DC-DC converter with high voltage gain for fuel cell hybrid system is proposed. The proposed converter integrates a fuel cell power supply port, an energy storage unit port, and a load port, which can realize energy conversion between any two of them. This means that it has a high level of integration and can achieve the energy management of the hybrid system. Meanwhile, the switched-capacitor unit enables the converter to have the advantages of high voltage gain and low voltage stress. According to the relationship between power sources and load, five different operating modes can be defined. Then, the circuit structure and operating principle of the different modes are analyzed. Finally, simulation verifications are given to illustrate the effectiveness and feasibility of the proposed converter

Advanced Robustness Control of DC-DC Converter for Proton Exchange Membrane Fuel Cell Applications

International audienceThe fuel cell output power depends highly on the random load profile, and power converters play a key role in fuel cell power system. Robust control design of the converter can help to design online control and optimize the diagnostic method for fuel cell based applications. In this paper, an advanced control algorithm of DC-DC converter for proton exchange membrane fuel cell (PEMFC) is realized through robustness algorithm based on Flatness Control and Active Disturbance Rejection Control (ADRC). Flatness control can track the power demand and ADRC can help to estimate the total required power in real-time. The effectiveness of the proposed control method is verified through a Two-phase Interleaved Boost Converter (TIBC), and results indicate the steady state error of TIBC's output voltage can be decreased. Besides, the proposed control scheme is not sensitive to system parameter variations, and it can balance the power among each phase accurately. Moreover, the Flatness-ADRC control can guarantee the smooth output of the converter when load disturbance occurs. The simulation and experimental results all indicate that strong robustness can be obtained when compared with conventional PI control method. The simple architecture of the controller makes it easier to be implemented in real-time online applications

A Literature Review and Result Interpretation of the System Identification of Arch Dams Using Seismic Monitoring Data

The system identification of concrete dams using seismic monitoring data can reveal the practical dynamic properties of structures during earthquakes and provide valuable information for the analysis of structural seismic response, finite element model calibration, and the assessment of postearthquake structural damage. In this investigation, seismic monitoring data of the Pacoima arch dam were used to identify the structural modal parameters. The identified modal parameters of the Pacoima arch dam, derived in different previous studies that used forced vibration tests (FVT), numerical calculation, and seismic monitoring, were compared. Meanwhile, different modal identification results using the input-output (IO) methods and the output-only (OO) identification methods as well as the linear time-varying (LTV) modal identification method were adopted to compare the modal identification results. Taking into account the different excitation, seismic input, and modal identification methods, the reasons for the differences among these identification results were analyzed, and some existing problems in the current modal identification of concrete dams are pointed out. These analysis results provide valuable guidance regarding the selection of appropriate identification methods and the evaluation of the system identification results for practical engineering applications

Open-Circuit Switch Fault Diagnosis and Fault-Tolerant Control for Output-Series Interleaved Boost DC-DC Converter

International audienceThis paper proposes a fault-tolerant control method for an input-parallel-output-series (IPOS) converter under open-circuit switch failure, which mainly focuses on two parts: fault diagnosis (fault detection and fault identification) and remedial action. The fault diagnosis is realized based on immersion and invariant observer (I&IO), which has strong robustness to parameter uncertainty and external disturbances, and therefore it can be designed using only the crude converter model with nominal parameters. Moreover, the sampling frequency required by the fault diagnosis is the same as the frequency required by the system controller. Thus, the fault diagnosis module can be easily embedded in the well-designed power system without extra sensors. Based on the method, the open-circuit fault in power switches can be detected and identified within two switching periods. As for remedial action, two redundant switches are needed for postfault reconfiguration. And the remedial action can be immediately triggered after the switch failure is detected. To reduce the complexity of remedial action, the same postfault reconfiguration will be carried out for the open-circuit failure in different switches. Besides, system controllers are also carefully designed to guarantee the performance of the postfault converter. Both simulations and experiments are conducted for the validations, and the results have shown the effectiveness, robustness, and rapidity of the proposed method

A Literature Review and Result Interpretation of the System Identification of Arch Dams Using Seismic Monitoring Data

The system identification of concrete dams using seismic monitoring data can reveal the practical dynamic properties of structures during earthquakes and provide valuable information for the analysis of structural seismic response, finite element model calibration, and the assessment of postearthquake structural damage. In this investigation, seismic monitoring data of the Pacoima arch dam were used to identify the structural modal parameters. The identified modal parameters of the Pacoima arch dam, derived in different previous studies that used forced vibration tests (FVT), numerical calculation, and seismic monitoring, were compared. Meanwhile, different modal identification results using the input-output (IO) methods and the output-only (OO) identification methods as well as the linear time-varying (LTV) modal identification method were adopted to compare the modal identification results. Taking into account the different excitation, seismic input, and modal identification methods, the reasons for the differences among these identification results were analyzed, and some existing problems in the current modal identification of concrete dams are pointed out. These analysis results provide valuable guidance regarding the selection of appropriate identification methods and the evaluation of the system identification results for practical engineering applications