Video Individual Counting (VIC) is a recently introduced task that aims to estimate pedestrian flux from a video. It extends conventional Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that only learns to count repeated pedestrian patterns across frames, the key problem of VIC is how to identify co-existent pedestrians between frames, which turns out to be a correspondence problem. Existing VIC approaches, however, mainly follow a one-to-one (O2O) matching strategy where the same pedestrian must be exactly matched between frames, leading to sensitivity to appearance variations or missing detections. In this work, we show that the O2O matching could be relaxed to a one-to-many (O2M) matching problem, which better fits the problem nature of VIC and can leverage the social grouping behavior of walking pedestrians. We therefore introduce OMAN, a simple but effective VIC model with implicit One-to-Many mAtchiNg, featuring an implicit context generator and a one-to-many pairwise matcher. Experiments on the SenseCrowd and CroHD benchmarks show that OMAN achieves the state-of-the-art performance. Code is available at OMAN.

The dysfunction of biological systems caused by disease-related genes is one of the inducements of complex diseases. To understand molecular mechanisms of complex diseases, the identification of disease-related gene modules in biological networks through community detection is emerging as a promising approach. However, most community detection methods are not suitable for biological networks because their topological structures are complex and the scale of biologically relevant modules are small. In this paper, a novel community detection method called MPSO-CD was proposed based on multi-objective particle swarm optimization, in which negative ratio association and ratio cut were employed as objective functions. Highlights of MPSO-CD are a mutation strategy based on clustering coefficient and the procedure of disease module screening referring to the internal connection density and functional similarity. Experimental results of social and synthetic complex networks indicate that MPSO-CD is comparable and often superior to four compared methods. Eventually, MPSO-CD is applied to the asthma gene co-expression network for identifying potential disease modules that provide the molecular mechanism information about asthma. Most of the captured modules have been proven to be associated with asthma through Gene Ontology and pathway enrichment analysis.

Natural fault-tolerance performance is becoming popular for dual three-phase permanent magnet synchronous motors (PMSMs) under open-phase fault, as it eliminates the need for control structure reconfiguration and fault diagnosis. Unlike the current constraint imposed by the open-phase fault, little attention has been given to the voltage relationship between the inverter and the motor. In this paper, aiming to address the voltage disturbance by this constraint, an improved universal control scheme has been proposed for dual three-phase PMSM under single open-phase fault. The voltage disturbances have been modelled as dc-type and periodic-type, and then the low-passing filter plus resonator-based disturbance observer has been utilized in control scheme of torque subspace for improving disturbance rejection. The simulation and experimental results are presented to illustrate the effectiveness of the proposed method.

Fault-tolerant control (FTC) strategy can be realized without modifying the peripheral hardware circuit when the open-circuit fault (OCF) occurs in the multiphase motor. However, FTC relies on accurately identifying the fault location and switching to a new reconfiguration fault-tolerant algorithm. This can significantly increase the complexity of the system. To overcome the challenge, this article investigates a passive fault-tolerant scheme of a 2 × 3-phase surface-mounted permanent-magnet synchronous motor (SPMSM) driven by a mono-inverter when single-phase OCF occurs. The state equations based on field-oriented control of 2 × 3-phase SPMSM under healthy and single-phase OCF are discussed. The special motor drive mode and the constraint ofid= 0 make the phase currents of each module passively optimized under the two neutral point configurations (i.e., isolated or connected), thus meeting the demand of restraining torque ripple. In the proposed PFTS, when the OCF occurs, the system does not require to attempt to diagnose or correct faults. Hence, a seamless transition from normal to faulty operation is guaranteed. Moreover, it enhances system reliability and stability. Furthermore, taking an existing 2 × 3-phase SPMSM as an example, the experiments are carried out for validation.

The inverter nonlinearity induces current harmonics and mismatches between permanent magnet synchronous machine reference voltages and terminal voltages, which will degrade the sensorless drive system performance, especially at the low-speed range. In this paper, a radial basis function neural network (RBFNN)-based voltage compensator is proposed to suppress the current ripple. Without the requirement of any additional hardware or complex signal analysis procedure and processing algorithm, the RBFNN is self-tuned to directly generate the compensation voltage with the objective to minimize the current tracking error, so as to improve the active flux modeling accuracy and reduce position and speed estimation fluctuation.

BACKGROUND: Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. RESULTS: In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. CONCLUSIONS: The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

Sliding-mode observer (SMO) has attracted extensive attention in the field of medium- and high-speed sensorless control of permanent magnet synchronous motor (PMSM) because of its strong robustness and stability. However, the traditional methods are vulnerable to dc bias caused by measurement errors and parameter changes. Therefore, in this article, an improved SMO algorithm by disturbance observer compensation is proposed. The algorithm unifies the mathematical models of surface PMSM and interior PMSM. Besides, a bandpass filter (BPF) is used to replace the traditional low-pass filter, so it can effectively suppress dc bias and high-frequency noise. In addition, at any BPF center frequency, the proposed disturbance observer with low-pass filter (LPF) characteristics can perfectly compensate the position error caused by the digital filter in real time. Moreover, through sensitivity analysis, the influence of model uncertainty on the observation position is studied, and an adaptive extended state observer is added to mitigate the influence of parameter mismatch on the performance, hence improving the estimation accuracy. Finally, a triple redundant permanent magnet-assisted synchronous reluctance motor is taken as an example to verify the feasibility and effectiveness of the proposed observer.