Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Concept Extraction for Zero-Shot Video Retrieval

For a two-year period, during 2016 and 2017, we tackled an ad-hoc video search (AVS) task, which is one of the research tasks in the TRECVID benchmark. The goal of this AVS task is to conduct a detailed video retrieval from a large-scale video database using a query phrase. We achieved the top accuracy for the second consecutive year by combining a large number of pre-trained concept classifiers. However, the best performance of the AVS system remains very low at around 20%, and appropriate video sequences cannot be retrieved at all for certain specific query phrases. In this paper, we investigate whether such an extremely poor performance can be improved in the following two ways: (1) unveiling latent concepts from existing concept classifiers, and (2) combining multiple concept classifiers obtained using visual features.
High-Frequency LLC Resonant Converter With Magnetic Shunt Integrated Planar Transformer

Achieving high efficiency and high power density is emerging as a goal in many power electronics applications. LLC resonant converter has been proved as an excellent candidate to achieve this goal. To achieve smaller size of passive components, the resonant inductor in the LLC converter is usually integrated into the transformer by utilizing its leakage inductance. However, the leakage inductance of the transformer is usually insufficient and thus the LLC converter has to be operated in a limited frequency range (this limits the input voltage range accordingly), otherwise the power efficiency will drop dramatically. Therefore, a larger resonant inductance in the LLC converter is expected to operate in a wider input voltage range. This paper proposes a new method to create a larger resonant inductance by using a magnetic shunt integrated into planar windings. The accurate leakage inductance modeling, calculation, and optimal design guideline for LLC planar transformer, including optimal magnetic shunt selection and winding layout, are presented. A 280–380 V input and output 48 V–100 W half-bridge LLC resonant converter with 1 MHz resonant frequency is built to verify the design methodology. A comparison is made between two converters with the same parameters, one using magnetic shunt integrated transformer and the others using traditional planar transformer and external inductor. Experimental result shows the proposed converter with magnetic shunt is capable to achieve comparable high efficiency and regulation capability with the other under a wide input voltage, which verifies the optimal design methodology. Above all, this magnetics integration methodology reduces the whole converter's volume and thus increases the power density.
Airborne Particle Detection Based on a Spin Hall Effect Measurement

A particle detection approach that measures a change of a spin-dependent photocurrent caused by a particle is proposed. The concept is evaluated through step by step mathematical analysis of an airborne particle sensing process using an inverse spin Hall effect (ISHE) device and a circularly polarized incident light, and an equation is derived to reveal the key parameters that affect the particle detection. The theoretical model predicts that the detection signal of a submicron particle will be on the scale of nanovolts when using a large ISHE sensor of a 10−4 V signal without the particle disturbance, indicating that some submicron particles are likely detectable with a commercially available nanovoltmeter if background noise can be suppressed. The derived model further shows that the detection sensitivity can be increased significantly by decreasing sensor pixel size. This contrasts from conventional approaches to increase sensitivity using expensive high-power short-wavelength lasers or bulky optical components, allowing this proposed sensor to be more compact, cost effective, and power efficient than existing photon counters. In addition, the theoretical model suggests that the sensor can have multiple controllable knobs in the same device, which could be utilized to suppress background noise and perform comprehensive particle analysis. Because the proposed approach is to detect changes in light intensity, degree of circular polarization, and helicity caused by a particle in the same device, the approach can potentially provide better detection capabilities than existing techniques and address challenges in mobile, wearable, and process equipment applications.
An Improved Rolling Horizon Evolution Algorithm with Shift Buffer for General Game Playing

General Game Playing (GGP) is the design of artificial intelligence programs to play more than one game. Here, one of the most famous GGP frameworks, The General Video Game AI Competition (GVGAI) Framework, is used in order to design controllers for Atari 2600 inspired games. Recent advancements in the literature of GVGAI showed that the Rolling Horizon Evolution Algorithm (RHEA) is competitive when compared to other methods, encouraging the use and the research by improvements for this method. The use of a 1-Step-Look-ahead approach and a Redundant Action Avoidance policy during the creation of new individuals are proposed in this paper. The 1-step-look-ahead technique improves the action selection after the shift of the individual in RHEA with the shift buffer enhancement (RHEA-SB), and the redundant action avoidance policy decreases the chance of spatial redundant actions within the individual. Also, a parameter analysis of RHEA-SB is performed here, where different values of population size, depth of simulations, and number of individuals that remains in the population are evaluated. Results show that using 1-Step-Look-ahead and a redundant action avoidance policy improves the quality of the solutions found when compared to the original algorithm.
Joint Power Allocation and Transmission Time Selection in Duty-cycled Wireless Sensor Networks

Energy-aware resource allocation plays an important role in battery-limited wireless sensor networks (WSNs), as it improves network performance, in terms of energy savings, and prolongs network lifetime as well. In this paper, we study a duty-cycled WSN where the sensor nodes transmit their data simultaneously to a central access point and then return to sleep mode (this process is repeated in each time period). In particular, we propose a joint power allocation and transmission time selection algorithm in order to minimize the maximum energy consumption of the sensor nodes, under power and time constraints as well as quality-of-service (QoS) requirements. In the problem formulation, we also take into account the circuit energy consumption of each sensor node. Moreover, the proposed algorithm, which is based on a successive convex approximation method, converges very quickly and achieves a Karush-Kuhn-Tucker (KKT) solution for the problem. Finally, numerical results reveal that the proposed technique provides significant performance gain when compared to a baseline scheme.
An Evaluation of Potential Rise in a Wind Turbine Generator Earthing System During a Direct Lightning Strike

A reliable and robust earthing system design is critical for the lightning protection system of a wind turbine. This paper presents an analysis of the wind turbine earthing impedance and potential rise for a horizontal multi-layer soil model in the event of a direct lightning strike on the wind turbine generator. The influence of soil resistivity and lightning current wave shapes on the wind turbine grounding impedance are analysed. The results show that the wind turbine earthing system is more vulnerable to high frequency components of the lightning strikes. The other findings highlight the impact of soil resistivity values and frequencies over the impedance and potential distribution of the wind turbine generator earthing system.
Design of a Contactless Rotary Device Based on Inductively Coupled Power Transfer

Contact-type rotary electronic devices have the disadvantages of easy wear and no electrical isolation, which is not conducive to the safety and reliability of the device. In this paper, a contactless rotary inductively coupled power transfer (ICPT) device has been proposed, which provides electrical isolation, higher flexibility and greater security. The device consists of a rotary loosely coupled transformer, S/LCC compensated resonant converter and a DC-DC converter. To verify the analysis and design, a 400W rotatable prototype with a 50mm gap was fabricated and tested. An overall efficiency 84.3% was achieved from DC 40V input to DC 40 V output.
An Integrated Crowdsourced Framework for Disaster Relief Distribution

This paper outlines our ongoing efforts at building an effective disaster relief system utilizing real-time, crowdsourced responses to disaster events. This system integrates three components: social media analysis, rescue demand prediction, and relief distribution optimization. We introduce the conceptual disaster relief system, database structure, and its integrated components. Finally, a summarization of the current research challenges we expect to address in the future is provided.
High Performance Facial Expression Recognition System Using Facial Region Segmentation, Fusion of HOG & LBP Features and Multiclass SVM

Facial expression is an effective method of nonverbal communication and expressing feelings. A method to recognize emotions through facial expressions is proposed in this paper. After some preprocessing of the input image, the facial region is segmented into four expression regions according to the highly effective proposed segmentation method. Features from these segmented parts are extracted using a fusion of Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). Reduction of the dimension of the feature vector is done using Principal Component Analysis (PCA). For classifying the features and thus the expression images, multiclass Support Vector Machine (SVM) is used. The performance of the proposed method is measured using three publicly available and highly used datasets (JAFFE, CK+, RaFD). Finally, achieved performance is compared with performance on these datasets by other available methods to indicate that the proposed method succeeds in achieving state-of-the-art performance.
Modeling and Control of Stand-alone PV System Based on Fractional-Order PID Controller

In this paper, an efficient energy management scheme for stand-alone photovoltaic microgrid system (SAPVMS) is elaborated. The proposed fractional order (FO) PID and PI based energy management scheme comprises of two control loops namely inner and outer loops to outperform a standalone PV system with satisfactory response. The main task of the inner control loop is to extract and track maximum power from SAPVMS with sufficient tracking behavior. Moreover, the outer control loop is equipped with the battery for controlling the power between the source and the load smoothly. Particle swarm optimization (PSO) is working to fine tune the proposed control loops by generating their optimal settings. The integral of the absolute error multiplied by time is utilized to define the proposed system objective function. Various scenarios include sun irradiance variations and load fluctuations are presented to demonstrate the theoretical analysis, effectiveness of the offered energy management strategy. Further validations for numerical results attained by PSO are made compared with classical controllers such as PI and PID which signify the tuned FOPID and FOPI gains. The model results of the entire SAPVMS are described given along with comprehensive simulation results that discover the feasibility of the system.
Research and Implementation of Personalized Clothing Recommendation Algorithm

Research of the clothing recommendation algorithm is important that can be used to provide a more efficient method for consumers to select their expected clothing. Considering the characteristics of clothing product, in this paper, a personalized clothing recommendation algorithm based on fine-grained attributes is reported. In this method, the fine-grained attributes of the clothing are established based on clothing image. And a personalized clothing attributes preference model for each user combining with the fine-grained attributes of the clothing and personal parameters is built. This method is used in an application system based on client/server framework and a mobile phone software based on the Android platform.
Integrated Indicator Approach for Economic-Environmental Assessment of Coastal Local Municipalities

Development of coastal local municipalities is related to economic benefits and environmental issues. Increasing human populations, development of marine infrastructure and traffic, etc., have led to significant increase of anthropogenic pressure on the coastal environment of local municipalities in the St. Petersburg and Leningrad region. On the other hand, designating new protected areas can provide means to reduce negative impacts of human activities in coastal environments. Coastal local municipalities within these regions were compared in spatial and temporal scales, based on an integrated indicator approach. Trends of anthropogenic pressure were shown and environmental protection actions to mitigate the negative impacts of this pressure in the Eastern Gulf of Finland were discussed. The integrated indicator approach is based on socio-economic and environmental aspects. Dimensionless indicators, which are calculated for coastal local municipalities of the Leningrad region and for four coastal municipal regions of St. Petersburg, provide an opportunity to compare and analyze trends of municipality conditions on a comparative basis. In this paper, results of calculation and assessment of environmental and economic indicators for coastal local municipalities are made for the period between 2008 and 2016.
Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method

In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link robotic manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.
dSP ACE DS 1202 Based Real Time Implementation of Cuckoo Search Optimized FDTC of PMSM

Recently, direct torque control (DTC) is advised as a control strategy to be used in motors drives. Latest developments in artificial intelligence based control have brought in to focus a possibility of optimizing all the control parameters. In this research work a Fuzzy Logic (FL) speed controller is used as the outer control loop through a permanent magnet synchronous motor (PMSM) DTC where the inner control loops are the flux and torque controllers. All controllers' parameters of both inner and outer loops are well-optimized using Cuckoo Search (CS) algorithm. For the real time implementation of the overall system, dSPACE DS1202 is used. and MATLAB-SIMULINK is used to perform the simulation model to offer simulation of dynamic and steady state response. For both practical work and simulation, the system is tested at different operating conditions. A comparison between practical and simulation is performed and shows a good agreement between experimental and simulation results with high dynamic performance and steady state achievement in short time.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation

Nowadays, the recommendation systems are applied in the fields of e-commerce, video websites, social networking sites, which bring great convenience to people’s daily lives. The types of information are diversified and abundant in recommendation systems; therefore the proportion of unstructured multimodal data such as text, image, and video is increasing. However, due to the representation gap between different modalities, it is intractable to effectively use unstructured multimodal data to improve the efficiency of recommendation systems. In this paper, we propose an end-to-end multimodal interest-related item similarity model (multimodal IRIS) to provide recommendations based on the multimodal data source. Specifically, the multimodal IRIS model consists of three modules, i.e., multimodal feature learning module, the interest-related network (IRN) module, and item similarity recommendation module. The multimodal feature learning module adds knowledge sharing unit among different modalities. Then, IRN learns the interest relevance between target item and different historical items respectively. Finally, the multimodal feature learning, IRN, and item similarity recommendation modules are unified into an integrated system to achieve performance enhancements and to accommodate the addition or absence of different modal data. Extensive experiments on real-world datasets show that, by dealing with the multimodal data which people may pay more attention to when selecting items, the proposed multimodal IRIS significantly improves accuracy and interpretability on top-N recommendation task over the state-of-the-art methods.
Frequency Optimization for Inductive Power Transfer Based on AC Resistance Evaluation in Litz-Wire Coil

A coil with a high quality factor <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> is desired to obtain a high efficiency for inductive power transfer (IPT). <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> is proportional to the coil inductance and operating frequency, while it is inversely proportional to the coil resistance, which increases with frequency. An optimized frequency exists to achieve the maximum efficiency. Eddy currents and resulting ac resistance in Litz-wire coils are attributed to magnetic field. Especially, the induction component of the ac resistance is approximately proportional to the squared magnetic field to which the coil is exposed to. FEA simulations are conducted and surface integral method is employed to determine the squared field. Additionally, the volume integral method is proposed to evaluate the overall effect of the field on the induction resistance. The optimized frequency for the maximum efficiency is obtained, using the squared field calculation and resulting ac resistance evaluation. Sample prototype coils are manufactured to verify the resistance analysis methods. An IPT system is built employing these coils. Experiments show that the IPT system achieves the highest efficiency at frequencies closed to the predicted optimized ones.
Data Encoding for Byzantine-Resilient Distributed Gradient Descent

We consider distributed gradient computation, where both data and computation are distributed among m worker machines, t of which can be Byzantine adversaries, and a designated (master) node computes the model/parameter vector, iteratively using gradient descent (GD). The Byzantine adversaries can (collaboratively) deviate arbitrarily from their gradient computation. To solve this, we propose a method based on data encoding and (real) error correction to combat the adversarial behavior. We can tolerate up to$t\leq \displaystyle \lfloor\frac{m-1}{2}\rfloor$ corrupt worker nodes, which is information-theoretically optimal. Our method does not assume any probability distribution on the data. We develop a sparse encoding scheme which enables computationally efficient data encoding. We demonstrate a trade-off between the number of adversaries tolerated and the resource requirement (storage and computational complexity). As an example, our scheme incurs a constant overhead (storage and computational complexity) over that required by the distributed GD algorithm, without adversaries, for$t\leq \displaystyle \frac{m}{3}$.
Supervisory Control Approach and its Symbolic Computation for Power-Aware RT Scheduling

Safety-critical systems implemented on multicore platforms need to satisfy stringent power dissipation constraints such as thermal design power (TDP) thresholds used by chip manufacturers. Power dissipation beyond TDP may trigger dynamic thermal management (DTM) in order to ensure thermal stability of the system. However, the application of DTM makes the system susceptible to higher unpredictability and performance degradations for real-time tasks. This paper proposes a formal scheduler synthesis framework that guarantees adherence to a system level peak power constraint while allowing optimal resource utilization in multicores. Our proposed framework makes use of supervisory control of timed discrete event systems as the underlying formalism. All steps starting from individual models to construction of the scheduler have been implemented through binary decision diagram based symbolic computation, so that the state-space complexity associated with the framework may be controlled. Furthermore, the synthesis framework has been extended to handle tasks with phased execution behavior. Conducted experiments have shown promising results and indicate to the practical efficacy of our approach.
Task-Space Control of Articulated Mobile Robots With a Soft Gripper for Operations

A task-space method is presented for the control of a head-raising articulated mobile robot, allowing the trajectory tracking of a tip of a gripper located on the head of the robot in various operations, e.g., picking up an object and rotating a valve. If the robot cannot continue moving because it reaches a joint angle limit, the robot moves away from the joint limit and changes posture by switching the allocation of lifted/grounded wheels. An articulated mobile robot with a gripper that can grasp objects using jamming transition was developed, and experiments were conducted to demonstrate the effectiveness of the proposed controller in operations.
Capacitor Voltage Shaper for Cascaded H-Bridge StatCom

In this paper, an active shaping method of the capacitor voltage for a cascaded H-bridge (CHB) Iow-capacitance static synchronous compensator (LC-StatCom) is introduced. The traditional LC-StatCom is able to operate with large capacitor voltage ripples when it is operating with rated current. However, as the current is reduced below rated current, voltage ripple on the capacitors decreases. Decreasing the current therefore removes the advantages of the LC-StatCom such as better current quality and reduced switching losses. The active shaping method aims to keep consistent large voltage ripple on the capacitor using a simple boost converter cell to inject an appropriate auxiliary capacitor current. Simulation results are provided to confirm the performance of the proposed CHB-StatCom system.
Temporal Frame Interpolation With Motion-Divergence-Guided Occlusion Handling

We present a high-quality temporal frame interpolation (TFI) method that employs piecewise-smooth motion and handles disoccluded regions using the observation that motion discontinuities travel with the foreground object. We derive a “motion discontinuity” likelihood map from the divergence of a motion field between the input frames. Motion which is modeled at the reference frame is mapped to the target frame using a cellular-affine mapping strategy—a process during which regions of disocclusion are readily observed. This information is then used to guide the occlusion-aware, bidirectional FI process. Furthermore, we propose two computationally inexpensive texture optimizations that selectively improve the quality of the interpolated frames in regions around moving objects. The scheme produces very high-quality interpolated frames and outperforms current high-quality state-of-the-art TFI schemes by 2–2.5 dB; the method works with a very low-complexity motion estimation scheme and runs orders of magnitudes faster than its competitors.