Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF USING DDGS AND AVIZYME ENZYME IN THE BROILER DIETS ON SOME PHYSIOLOGICAL RESPONSES

This experiment aimed to study the effect of using distillers dried grains with soluble (DDGS) at different levels (0, 5, 10, 15%) treated with or without enzyme (avizyme1500) at level 0 and 1g/ kg diet on some immunological and physiological responses of broiler chicks. Two hundred and sixty five, Arbor acres unsexed broiler day-old chicks, were randomly divided into eight groups with 32 birds per each. The sequence of the eight dietary treatments were as follow: T1Basal diet (without DDGS or enzyme); T2, T3 and T4Basal diet contains 5, 10 and 15% DDGS without enzyme; T5, T6, T7 and T8Basal diet contain 0, 5, 10, 15% DDGS with enzyme. The results showed that blood proteins for birds fed dietary DDGS at different levels significantly (p<0.01) increased than those fed control diet. The means of total lipids, triglyceride, cholesterol, AST and ALT in the broilers fed dietary DDGS (p<0.01) differed, while creatinine value was insignificantly affected. The means of AST values for broilers fed the diet containing enzyme significantly (p<0.01) decreased, while ALT level was not affected. Similarly, the means of T4 hormone concentration for broiler fed diet with adding enzyme significantly (p<0.01) reduced, while T3 insignificantly reduced compared with control diet. The mean PCV% (p<0.01) increased while, Hb reduced significantly (p<0.01) compared with control diet while, no significant differences were detected in RBC's and WBC's counts. From the present results it can be concluded that using grade levels (5, 10 and 15%) of DDGS in broiler diets as untraditional ingredient (as a replacement of corn and soybean ingredients of diet) led to improve the physiological and immunological responses.
Study on the Tremor Elimination Strategy for the Vascular Interventional Surgical Robot

For interventional vascular surgery, which requires a high degree of accuracy, any minor error could lead to the failure of the operation. High stressful operation environment leads to hands’ tremor of doctors, which is fatal to patients. A recognition and elimination strategy for hands tremor was proposed. Hands’ tremor was divided into two types, the first type is normal physiological tremors and the second type is stress tremor. First, we used the regularity of normal physiological tremor to identify and eliminate it. Secondly, according to the safe contact force index in vascular interventional surgery, the safe acceleration of the robot was obtained by using Newton’s second law. Stress tremor was identified by comparing actual acceleration with safe acceleration. The effectiveness of the strategy was verified by experiments in which multiple operators simulated the operation of doctors. Experimental results show that the tremor elimination strategy has better performance.
AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping

This paper introduces a new dataset dedicated to multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). This dataset consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements. They also exhibit explicit direct interactions between robots through the detection of mounted AprilTag markers [1]. Ground-truth trajectories for each robot were computed using Structure-from-Motion algorithms and constrained with the detection of fixed AprilTag markers placed as beacons on the experimental area. Those scenarios have been benchmarked on state-of-the-art monocular, stereo and visual-inertial SLAM algorithms to provide a baseline of the single-robot performances to be enhanced in collaborative frameworks.
Cost Function-Based Open-Phase Fault Diagnosis for PMSM Drive System With Model Predictive Current Control

Open-phase fault (OPF) in the permanent magnet synchronous machine (PMSM) drive system is one of the most prevalent failures due to the resulting large electromagnetic torque ripples and serious mechanical vibration. To improve the reliability of the PMSM drive system, several OPF diagnosis methods were studied and were mainly focused on vector control system. Recently, model predictive control (MPC) has been emerging as a popular control method for the PMSM drive system owing to its simple structure and excellent dynamic performance. However, so far, there are no special OPF diagnosis methods for the model predictive controlled PMSM drive system. Hence, this article first proposes a cost function-based OPF diagnosis method for the model predictive controlled PMSM drive system, considering the characteristics of MPC system. The dc component and second harmonic component in the cost function are used for the OPF diagnosis, and the initial phase angle differences are defined to locate the faulty phase. The experimental results show that, by the proposed fault diagnosis method, not only the OPF can be rapidly detected, but also the faulty phase can be effectively located. Moreover, the proposed method can be implemented simply and is robust against the operating point and parameter variations of the PMSM drive system. This article proposes an OPF diagnosis method for the model predictive controlled PMSM drive system based on cost function, taking into account the characteristics of the MPCC system. The experimental results verify the effectiveness of the proposed method. From the theoretical analysis and experimental results, the following works and conclusion are drawn.
A Robust Multi-Channel EEG Signals Preprocessing Method for Enhanced Upper Extremity Motor Imagery Decoding

Brain computer interface (BCI) based on non-invasive electroencephalography (EEG) signals has become a promising and alternative method for electromyography (EMG) signal in the rehabilitation process of amputees with high level of amputation. This is because their residual muscles cannot provide sufficient myoelectric signals to accurately recognize limb movement intentions. One of the major challenges of BCI based EEG methods is the inevitable artifacts contained in multi-channels EEG recordings that would affect accurate characterization and decoding of limb movement intents from brain signals. Previous studies have applied different methods based on regression, blind source separation (BSS) and filtering techniques, though with limited efficacy in fully isolating artifacts from the entire EEG signals. Also, some of the existing methods are targeted towards the removal of one or two particular types of artifacts. In this study, a linear algorithm based on generalized eigenvalue decomposition and multi-channel Wiener filter (GEVD-MWF) was proposed to simultaneously remove different type of artifacts inherent in EEG signals, recorded from four transhumeral amputee subjects. Experimental results showed that the proposed method outperformed the commonly used approach which achieved an improved average classification accuracy of 22.03% across all the classes of motor imagery (MI) tasks and subjects. For real-time applications, a modified channel selection algorithm-based on sequential forward floating selection (mSFFS) was adopted to remove redundant channels and an average accuracy of 96.87% was achieved with 10 electrode channels located at the pre-motor cortex, primary motor cortex and somatosensory cortex of the brain. The overall performance of the proposed method suggested that multiclass MI tasks can be reliably and accurately characterized using artifact-free EEG signals from a few number of channels. Thus, the proposed methods can be a potential control method for rehabilitation devices for individuals with high level amputation.
Anomaly Detection on Broadband Network Gateway

Anomaly detection in Digital Subscriber Line (DSL) networks is a vital task to immediately detect unusual network behavior caused by cyber security threats, faulty hardware or software. Generally, to make this process automatic, state-of-the-art methods use machine learning techniques to analyze data collected from either Customer Premises Equipment (CPE) or from devices in Access Network. In contrast to the existing methods, this paper utilizes network traffic data collected from multiple static Broadband Network Gateways (BNGs) which are core network devices at Network Service Provider (NSP). To automatically detect anomalies in BNG traffic data, a new framework is proposed which consists of three steps: data acquisition, feature extraction, and modeling anomalies using a random forest-based framework. The proposed method is compared with state-of-the-art methods and the results show the effectiveness and robustness of our method.
A Hybrid Recommender System for Sequential Recommendation: Combining Similarity Models With Markov Chains

With the growth of artificial intelligence technology, the importance of recommender systems that recommend personalized content has increased. The general form of the recommender system usually analyzes the users’ log information to provide them with contents that they are interested in. However, to enable users to receive more suitable and personalized content, additional information must be considered besides the user’s log information. We develop, in the present study, a hybrid recommender system that unifies similarity models—collaborative and content-based—with Markov chains for a sequential recommendation (called U2CMS). U2CMS takes into account both sequential patterns and information about contents to find accurate relationships between items. It uses a higher-order Markov chain to model sequential patterns over several time steps, as well as the textual information of the content to model the recommender system. To show the effectiveness of the U2CMS—with regard to handling sparsity issues, different N-ordered Markov Chain, and accurately identifying similarities between items, we carried out several experiments on various Amazon datasets. Our results show that the U2CMS not only has superior performance compared to existing state-of-the-art recommendation systems (including deep-learning based systems), but also it successfully handles sparsity issues better than other approaches. Moreover, U2CMS appears to perform stable when it comes to different N-ordered Markov Chain. Lastly, through visualization, we show the success of our proposed content-based filtering model in identifying similar items.
Performance Analysis Graph-Based Keyphrase Extraction in Indonesia Scientific Paper

The increasing number of research papers from Indonesia and the higher the need to manage large amounts of documents, including memory capacity and the availability of time to manage research documents, keyphrase can provide a picture representing a text document. Thus, the management of documents in large quantities will become easier to prepare and find a collection of documents. For this reason, proper and correct keyphrase extraction can assist in managing paper documents. This study tried to compare three extraction methods using unsupervised graph-based techniques, including TextRank, TopicRank, and PositionRank. We compare the extraction results with the keyphrase made by the authors of the paper to see the three method’s performance. From the results of our research, we found that the method TextRank Providing the highest performance compared to other methods using the ROUGE test with average Recall, Precision, and F1-Score are 0.558, 0.71, and 0.225. However, the extracted phrases are longer compared to others. The performance of TopicRank, although it does not provide higher results with TextRank, the extracted keyphrase is more compact than TextRank and PositionRank methods.
On the Channel Estimation Performance of NOMA Systems: Experimental Implementation of Real-Time Downlink NOMA-OFDM

Due to the exponential growth in the number of connected devices, the utilization of available resources becomes challenging. To meet this demand, novel techniques with significantly higher gain for efficient spectrum usage and reduced latency are required. Non-orthogonal multiple access (NOMA) is expected to be a promising solution for the scarcity of spectrum resources, which are shared in a non-orthogonal way such that multiple users can access the same frequency and time slots simultaneously. Although many studies concentrate on NOMA, a few of them give insight into how the channel estimation and channel quantization affect the performance of the system. Related to this issue, this study focuses on a downlink NOMA implementation. In the first part, we mainly concentrate on the analysis of the effect of channel estimation and channel quantization on the bit error rate (BER) performance with MATLAB simulations. For the second part, the BER performance of the system is analyzed through a practical two-user downlink NOMA system implemented with software-defined radios (SDRs).
Statistical and Nature-Inspired Modeling of Vehicle Flows by Using Finite Mixtures of Simple Circular Normal Distributions

The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal (von Mises) statistical distributions. The parameters of the distributions are learned from empirical data by two variants of the expectation-maximization (EM) algorithm and by a nature-inspired method, differential evolution (DE). A proposed statistical model and a fitting strategy are evaluated on real-world data sets describing traffic flows in New York City. The experimental results show that the EM algorithm is able to find model parameters that correspond to input data and that are better than their analytic estimates, while DE evolves even more accurate models. The models based on circular distributions can be represented by circular plots as a novel type of visually appealing and easily interpretable fingerprints of the underlying traffic flow patterns.
Image Segmentation Method of Crop Diseases Based on Improved Segnet Neural Network

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters; secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training; in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it; then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network; finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s; compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper.
Short-term Photovoltaic Power Prediction based on DE-GWO-LSTM

In order to solve the uncertainty and randomness of photovoltaic output prediction caused by weather factors, and improve the accuracy of short-term photovoltaic power prediction, this study uses long short-term memory (LSTM) to establish a prediction model, and trains historical time series data to predict short-term photovoltaic power. First, a layer-by-layer training algorithm is used to construct a multi-hidden-layer network structure with excellent performance in regression prediction analysis. By analyzing the characteristics of the LSTM network function, a photovoltaic output prediction model is established. Then, the grey wolf optimization (GWO) algorithm of differential evolution algorithm (DE) is proposed to optimize the connection weight of LSTM to prevent the local minimum caused by random initialization, overcome the shortcomings of back-propagation algorithm used in traditional LSTM network, and improve the prediction accuracy of LSTM network. Finally, the simulation experiment of the improved LSTM model is carried out to test the prediction effect of the model under three kinds of weather conditions to prove the effectiveness of the model.
Fused: Closed-Loop Performance and Energy Simulation of Embedded Systems

Energy-driven computing is an emerging paradigm that aims to fuel the proliferation of tiny and low-cost IoT sensing and monitoring devices. Energy-driven computers are generally powered by energy harvesting sources, and adapt their operation at runtime according to energy availability; thus, they must be designed and tested according to the expected dynamics of their power source. However, today's processor simulators and debuggers typically assume that power is always available, so they are unable to correctly model the interactions between power supply, power consumption and energy-driven execution. To address this shortcoming, we propose Fused, an open source full-system simulator for energy-driven computers. Fused models execution, power consumption, and power supply in a closed loop, thus correctly models the interaction between them. It targets energy-driven embedded systems, and employs SystemC for digital and mixed-signal simulation to model a microcontroller and mixed-signal circuitry, enabling hardware-software codesign and design space exploration. Fused includes a high-level power modelling methodology, whereby events recorded during simulation are correlated to power measurements of real hardware to extract features for power modelling. Results show that Fused can model the execution time and power consumption of a commercially available microcontroller with a geometric mean error of 0.2% and 3.4% respectively, across a wide range of workloads. Through a case-study, we demonstrate that Fused can accurately model a state-of-the art intermittent computing system, where execution is heavily dependent on energy availability: although up to 70 power cycles were needed to complete the tested workload on the constrained energy supply, Fused modelled the completion time with less than 7% error.
An Energy-Saving Torque Vectoring Control Strategy for Electric Vehicles Considering Handling Stability Under Extreme Conditions

Four-wheel independently actuated electric vehicles (FWIA EVs) allow variable distributions of driving torques among individual wheels to improve vehicle performance. To reduce energy consumption while ensuring handling stability, we propose an optimal torque vectoring control strategy based on a two-level distribution formula. This strategy can naturally decouple front/rear axle torque vectoring from left/right torque vectoring and avoid the contradiction between stability and energy saving. First, considering the motor efficiency, the vehicle's total torque is optimally distributed to the front and rear axles based on model predictive control. Then, based on the front/rear axle distribution ratio, the left/right torque vectoring is revised to produce a suitable additional yaw moment to improve the handling stability. A sliding mode controller is designed to track the reference yaw rate calculated from a nonlinear reference model. The nonlinear reference model is more suitable for extreme conditions due to the accurate reflection of the nonlinear characteristics. A suitable additional yaw moment can ensure vehicle stability and avoid excessive energy consumption due to vehicle instability. The simulation and hardware-in-the-loop experimental results demonstrate that the proposed control strategy can reduce energy consumption while ensuring vehicle stability.
Motion Sequence Learning for Robot Walking Based on Pose optimization

Deep reinforcement learning provides a model-free method to teach a bipedal robot to walk, which is always a challenging task. However, low convergence and training efficiency limit the applications. To overcome such limitations, in this work an effective framework combined with traditional controllers and reinforcement learning is proposed, based on the realistic robot model Robotis-op3. The objective is to realize the motion sequence learning based on pose optimization (MSLPO). The Q-learning algorithm is used to train the walking parameters of the traditional model-based controller to get optimized poses. Based on pose optimization, deep reinforcement learning is utilized to train the walking controller. By combining pose optimization and reinforcement learning with reward shaping, the effective controller is achieved that can make the robot walk in a stable and fast manner. The proposed controller attains over 468% improvement in velocity and 51.5% decrease in relative deviation compared with the traditional controller. The proposed framework can also significantly improve the training efficiency compared with a continuous reinforcement learning algorithm. The proposed model is implemented in a simulated environment Pybullet and tested in different terrains. This work can be extended to other tasks and it is independent of a particular robot model.
An Efficient Massive MIMO Detector Based on Second-Order Richardson Iteration: From Algorithm to Flexible Architecture

Aiming at reducing the complexity of minimum mean square error (MMSE) detection in massive multiple-input multiple-output (MIMO) systems, this paper proposes a detection algorithm with high convergence rate and an efficient hardware architecture based on second-order Richardson iteration (SORI). In the proposed algorithm, a pre-iteration-based initialization method is presented to accelerate the convergence without extra complexity. In addition, the approximation of relaxation factor and the log-likelihood ratio (LLR) is introduced to further reduce computing load. Theoretical analysis demonstrates the advantages of the proposed algorithm in fast convergence and low complexity, and simulation results show that the proposed algorithm can efficiently approach MMSE performance. Based on this algorithm, a flexible hardware architecture is designed, which is deeply pipelined to support $128\times U$ ( $8\leq U\leq 32$ ) massive MIMO detection with the configurable number of iterations, and a folded dual-mode systolic array (DMSA) is fully utilized to achieve the flexibility with low hardware consumption. Implemented on Xilinx Virtex-7 FPGA and SMIC 40nm CMOS technology, the proposed detector is competitive in terms of energy and area efficiency compared to state-of-the-art iterative detectors, and it can adapt to the varied channel condition and the number of users in massive MIMO systems.