Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual mode modulation method for the full-bridge converter operating in a wide input voltage range

To improve the efficiency of full-bridge converter operating under wide input voltage conditions, this Letter proposes a dual mode modulation method. The full-bridge converter is controlled by phase-shifted mode at low input voltage and asymmetric pulse width modulation mode at high input voltage. The proposed method can reduce power loss, voltage stress of rectifier and output filter requirement. A 500-W full bridge converter with a 160 to 400 V input voltage range is built to confirm the validity of the proposed dual mode method.
ScholarWorks at WMU ScholarWorks at WMU Classifying Forest Composition using Fractal Dimension as an Classifying Forest Composition using Fractal Dimension as an Index of Image Texture Index of Image Texture

In this study, Landsat imagery from June (summer) and October (autumn senescence) 2000 of the area of Fort Custer Training Center in Kalamazoo and Calhoun Counties, Michigan, were analyzed for forest classification accuracies. The use of fractal analysis to improve forest classification, particularly to distinguish among northern hardwood species, was examined. Using moving windows with different sizes, measurement of local fractal dimension and spatial autocon-elation (Moran's I) were performed on the NDVIs and the panchromatic images. The measurement products were combined with Landsat TM bands as additional layers in supervised maximum-Ii kelihood multispectral classifications. The accuracy for multispectral classification of the October scene is 43.21 percent. The addition of the fractal dimension measurement on the October panchromatic image results in 45.06 percent accuracy, showing a slight improvement of 1.85 percent. The accuracy for the multispectral classification of the June scene is only 30.25 percent. However, the fractal dimension seems to work better on the June scene, since it gives a higher percentage of improvement. The addition of fractal dimension measurements on the June NDVI and panchromatic image gives 36.42 percent of accuracy, an improvement of 6.17 percent compared with using the spectral bands alone. found high for early successional sites (post-fire), decrease in mjd-succession, and increase as stands progress to uneven aged, uneven-height old growth. A similar type of study was performed They conducted a discrimjnation of the lodgepole pine forest development stages in the same area. This time, they used Landsat ETM+ data, and utilized a 2 nd order image texture derivative (entropy). The texture measure was based on the 15 m per pixel resolution of Landsat ETM+ panchromatic band. The image texture was combined with the NDVI image of the area, and resulted in about 30 percent improvement of classification accuracy with overall classification results of over 90 percent. The most significant improvement (36 percent) is for the uneven age, uneven canopy, Engelmann spruce (Picea engelmanni) and subalpine fir (Abies lasiocarpa) stands, which has proven to be a difficult age class to map. For the seedling and 100 year-old pine stands, the texture addition improved the accuracy by 12 percent.
Relation between knee extensors' strength, postural stability and variability of centre of pressure displacement during gait in adult women

Background: It has been shown that variability of walking is related to fall risk similarly as postural control and muscle strength. Joint potential of this group of variables for fall risk assessment is promising, however research interested in relations between them is lacking. Objective: The aim of this study was to investigate the relation between knee extensors' strength, centre of pressure (COP) velocity during one-leg stance and variability of COP displacement during various phases of gait cycle in middle-age women. Methods: A single group of 40 healthy women (age 56 ± 4.2 years) took part in the study. For assessment of knee extensors' strength (peak torque and average work during concentric and eccentric contractions) an isokinetic dynamometer was used. Mean velocity of COP during one-leg stance in anterior-posterior (AP) and medial-lateral (ML) directions was assessed on a force plate on a rigid surface with eyes open (two 30 s trials). Variability of COP displacement was assessed for loading response, midstance, terminal stance and preswing gait cycle phases (determined by vertical ground reaction force) in AP and ML directions. It was measured by two force plates positioned in the middle of an 8 m walkway (5 trials at a self-selected speed). For statistical analysis of relationships between variables Pearson correlation was applied. Results: Our results showed significant correlations between eccentric peak torque and COP velocity in AP direction during one-leg stance, eccentric and concentric peak torque and COP variability during loading response in both ML and AP directions and during terminal stance in AP direction. Conclusion: Loading response and terminal stance seems to be more related to knee extensors' strength. Variables derived from postural stability assessment during one-leg stance are independent from variables derived from assessment of COP displacement variability during walking.
perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games

Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. Therefore, Customer Lifetime Value (CLTV) is particularly vital for game companies to improve marketing decisions and increase game revenues. Nowadays, as virtual game worlds are becoming increasingly innovative, complex, and diverse, the CLTV of massive players is highly personalized. That is, different players may have very different patterns of CLTV, especially on churn and payment. However, current solutions are inadequate in terms of personalization and thus limit predictive performance. First, most methods just attempt to address either task of CLTV, i.e., churn or payment, and only consider the personalization from one of them. Second, the correlation between churn and payment has not received enough attention and its personalization has not been fully explored yet. Last, most solutions around this line are conducted based on historical data where the evaluation is not convincing enough without real-world tests. To tackle these problems, we propose a general system to predict personalized customer lifetime value in online games, named perCLTV. To be specific, we revisit the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion. On this basis, we develop a generalized framework to model CLTV across games in distinct genres by heterogeneous player behavior data, including individual behavior sequential data and social behavior graph data. Comprehensive experiments on three real-world datasets validate the effectiveness and rationality of perCLTV, which significantly outperforms other baseline methods. Our work has been implemented and deployed in many online games released from NetEase Games. Online A/B testing in production shows that perCLTV achieves a prominent improvement in two precision marketing applications of popup recommendation and churn intervention.
Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is “older” than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.
Children Treat Grammatical Errors Differently for Native and Non-Native Speakers

Both children and adults demonstrate biases against non-native speakers. However, in some situations, adults act more generously towards non-native speakers than towards native speakers. In particular, adults judge errors from non-native speakers less harshly, presumably because they expect such errors. In the present study, we asked whether 5-6-year-old children place less weight on errors from speakers with a foreign accent. In Experiment 1, 5- and 6-year-old children (N = 80) listened to pairs of either native or foreign-accented speakers (between-subjects) label objects. For native speaker pairings, children preferred information provided by grammatical speakers over information from speakers who made subject-verb agreement errors. In contrast, children chose between foreign-accented speakers at chance. In Experiment 2 (N = 40), children preferred information from grammatical foreign-accented speakers over information from foreign-accented speakers who produced word-order violations. These findings constitute the first demonstration that children treat speech errors differently based on a speaker’s language background.
Maritime availability of BDS/GNSS ARAIM in the Arctic

High-integrity navigation as an essential prerequisite of activity is worthy of being investigated in the Arctic, which can be achieved based on Global Navigation Satellite Systems (GNSS). The integrity performance of navigation in the arctic should be analyzed based on Beidou/GNSS in the arctic using Advanced Receiver Autonomous Integrity Monitoring (ARAIM) technology. Three different harbors in the Arctic were selected to simulate the changes of HPL under different combinations of constellation systems. The average distribution trend of HPL and availability at the polar region is also analyzed by using Stanford University's MASST software. The results show that the combination of two constellation systems can only meet the integrity requirements of ocean or coastal navigation. The availability of ARIAM is further improved using the combination of the three or four constellations, which can meet the high-integrity requirements of the ice navigation in the Arctic. In the case of BDS+Galileo+GLONASS and BDS+Galileo+GPS, the average HPL is 7.69m and 7.63m respectively. Using BDS+Galileo+GLONASS+GPS, its average HPL is 6.26m. All of them meet the requirement of HPL<10m.
A Multisensor-based Tightly Coupled Integrated Navigation System*

We present a tightly coupled multi-sensor integrated navigation and localization system based on graph optimization. To be specific, we calculate the preintegrated IMU factor by retraction strategy and use the reprojection error of the stereo camera as the visual landmark factor. Then we demonstrate their fusion with 3D line and plane primitives in the Lidar point cloud in a graph-based optimization framework. Furthermore, a segmented optimization algorithm is designed in the system and the widely used GPS and closure loop are added as constraints. The resulting visual-inertial-Lidar-GPS fusion navigation system is validated on a real-world dataset.
Transient Stability of Multi-Machine Wind Turbine Generators System Connected to the Power Network

currently worldwide installed capacity of the network connected wind generators grows rapidly; this rise of integration rate of wind energy could lead to circulation of transient stability and could potentially cause local or system wide blackout. This paper presents a recovery strategy that enables the system of HVDC transmission systems based on voltage source converter, which transmit electrical power from the wind turbines to the power network, to ride-through different positions of ac faults with smallest amount current and voltage stresses on the converter switching devices. Issue such as control strategies for a VSC-HVDC conduction system connecting offshore wind farms to the power network is discussed. The results show that the transient stability study of the system is widely different when the faults occur in neither wind side nor power network side. The DC link voltage behaves similarly to the AC voltage. However when the AC fault occurs in the wind side stations either for ac voltage or DC voltage the system behavior does not return to its normal operation after the fault is cleared. A reduced network model has been implemented in MATLAB/ SIMULINK to assess control performance during the fault.
MPC-based Tilting and Forward Motion Control of Quadruped Robots

In many practical implementations, a quadruped robot needs to safely pass long and narrow space. This paper investigates the tilting and forward motion control problem for a quadruped robot to pass narrow space. We develop a model predictive control (MPC)-based control method for the tilting and forward motion control. In particular, we firstly establish a quadruped robot floating centrifugal motion model, based on which we design objective functions and constraints in MPC to generate command forces for robots' feet. In addition, we design the gaits and reference trajectory of the roll angle to achieve tilting switching and forward movement. The effectiveness of the designed MPC algorithm is verified via the simulation platform and hardware experiments. Finally, we discover the feasible range of roll angle maintaining stable tilting and forward movement via simulation experiments.
Muscle-Specific High-Density Electromyography Arrays for Hand Gesture Classification

Objective: Dexterous hand motion is critical for object manipulation. Electrophysiological studies of the hand are key to understanding its underlying mechanisms. High-density electromyography (HD-EMG) provides spatio-temporal information about the underlying electrical activity of muscles, which can be used in neurophysiological research, rehabilitation and control applications. However, existing EMG electrodes platforms are not muscle-specific, which makes the assessment of intrinsic hand muscles difficult. Methods: Muscle-specific flexible HD-EMG electrode arrays were developed to capture intrinsic hand muscle myoelectric activity during manipulation tasks. The arrays consist of 60 individual electrodes targeting 10 intrinsic hand muscles. Myoelectric activity was displayed as spatio-temporal amplitude maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train cubic support vector machine machine-learning classifiers to classify the intended user motion. Results: Experimental data was collected from 5 subjects performing a range of 10 common hand motions. Spatio-temporal EMG maps showed distinct activation areas correlated to the muscles recruited during each movement. The thenar muscle fiber conduction velocity (CV) was estimated to be at 4.7±0.3 m/s for all subjects. Hand motions were successfully classified and average accuracy for all subjects was directly related to spatial resolution based on the number of channels used as inputs; ranging from 74±4% when using only 5 channels and up to 92±2% when using 41 channels. Temporal-spatial features were shown to provide increased motion-specific accuracy when similar muscles were recruited for different gestures. Conclusions: Muscle-specific electrodes were capable of accurately recording HD-EMG signals from intrinsic hand muscles and accurately predicting motion. Significance: The muscle-specific electrode arrays could improve electrophysiological research studies using EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions.
Overview of Axial-Flux Machines and Modeling Methods

With the increasing requirements and demands for electric machines in various applications, axial-flux machines (AFMs) attract more and more attention in recent years because of their unique merits of high power density, compact structure, and high material utilization. This article mainly investigates the topologies of AFMs and corresponding modeling methods. In general, AFMs can be classified according to different criteria. One classification is based on the machine configuration, and another one is based on their operational principles. The first classification discusses the characteristics of single-side AFMs, double-side AFMs, and multidisk AFMs. The second classification discusses the characteristics of conventional AFMs and emerging AFMs. As for the modeling methods, this article summarizes finite-element methods (FEMs) and analytical methods in detail. Based on these investigations, the article can provide the guidance and foresights for the design and analysis of AFMs.
Wavelength Selective Beam Switching Using Electrically Driven Nano-Strip MIM Tunnel Junctions

Tunable directional emission from electrically driven sources is essential for applications involving on-chip nano-circuits, sensing and quantum information processing. Here, we numerically demonstrate wavelength selective, switchable directional emission from periodic, nano-strip metal-insulator-metal tunnel junctions. Using two excitation sources, we show that our structure can efficiently redirect two different wavelengths in opposite directions when the excitation is switched. We achieved a peak directivity of 21.5 and 25.8 for wavelengths of 692 nm and 738 nm, respectively. The emission angle can be tuned by varying the periodicity, thereby paving the way for on-chip multiplexing.
A Novel Deep Reinforcement Learning Strategy in Financial Portfolio Management

Reinforcement learning algorithms are used in various fields widely, such as cryptocurrency market forecasting, image recognition, and natural language processing. In this research, we use the Reinforcement learning algorithm to solve the portfolio management problem. In the Reinforcement learning algorithm, we adopt the squeeze-and-excitation in the neural network neural network to realize the Ensemble of Identical Independent Evaluators proposed by Jiang et al. "The Squeeze-and-Excitation" block works by adaptively recalibrating channel-wise feature responses, which improves the ability of the network in extracting information from the financial environment. To further improve the performance of the network, we adopt the soft thresholding function as nonlinear transformation layers to effectively eliminate the noise-related features. The cryptocurrency market is used to test the efficacy of our strategy along with eight traditional portfolio management strategies as well as Jiang et al.’s strategies. In our experiments, we use the Accumulated Portfolio Value, Sharpe Ratio, and Maximum Downward to assess the efficacies of the strategies. In conclusion, our strategy outperforms Jiang et al.’s strategies and other traditional strategies. Although our strategy has a nearly 30% Maximum Downward as metrics in back-tests, Accumulated Portfolio Value can reach nearly 170% in two different two-month back-tests, which is about 160% greater than the traditional strategy and Jiang et al.’s strategy. Moreover, our result Sharp ratios are 393.88% and 96.18% higher than the traditional strategy and Jiang et al.’s strategy, respectively.
Early Forest Fire Recognition Method Based on C-GhostNet Network

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network.
Area Control of Unmanned Aerial Vehicles Swarm Based on Lanner-Jones Potential

Unmanned Aerial Vehicle (UAV) swarm area refers to the space occupied by a certain number of UAVs. By regulating the area of UAV swarm, the reflection area of radar is affected to reduce the exposure risk. To realize the effective modulation of UAV swarm area, inspired by the gas expansion phenomenon, an area modulation method of UAV swarm based on Lanner-Jones interatomic potential model is proposed. Firstly, the area measurement method based on the minimum convex hull algorithm is proposed to solve the bottleneck that the swarm distribution cannot be quantitatively analyzed. Secondly, on the basis of Vicsek flocking interaction model, the relative distance control method based on Lanner-Jones two-body interaction potential is proposed, which can realize the effective control of the global aera with the distance between two bodies as the unique variable. Finally, the simulation verification is carried out and the effects of the two control parameters named repulsion -distance and perception-distance on swarm aera is analyzed. The conclusion is that repulsion-distance is the dominant variable of swarm aera.