Miracle-dz/UniFAR-FTUs huggingface dataset:
Control and Fetching Strategy of Goods-Picking Robot in the Self-service Supermarket

The picking path of a six-degree-of-freedom (6 DOF) manipulator was planned according to the order requirement for the problem such as small working space and various goods in self-service supermarket. Different control and fetching strategies are adopted to enable the end-effector to contact the surface of the goods smoothly and quickly for efficient and accurate goods suction. The experiment indicates that the motion trajectory of the manipulator is consecutive and smooth without jumping point; and the average working efficiency is 24s per piece with the rate of successful picking is 98% which realizes the function of grabbing the target goods autonomously for the goods-picking robot system.
Evaluating the Linearized Feedback Controller for Regulation of Aided Sit-to-Stand in Subjects with Spinal Cord Injuries

Presented was an investigation on the performance of the feedback linearization control (FLC) method for the FES induced STS movement application in paraplegia. Improving the operation condition of the FES induced movement is one of the challenges currently receiving attention by so many scholars in the field. The Newton-Euler and Euler-Lagrange methods are utilised to check its correctness together with the muscle model proposed by Ferrarin and Pedotti. The FLC was evaluated for the system with and without fatigue disturbance. Although the fatigue disturbance was introduced in the system, results revealed that the cumulative current, cumulative rate of change in current the cumulative rate of change in current, RMSE, tracking error, cumulative tracking error and the level of the control signal, were by far better for the FL than that of the PID control technique with wide margins. Therefore, results portrayed that the FL control system the tendency to annul the effects of the effect of fatigue with higher capability over the gain scheduling based PID control method. It also indicates superiority in terms of system stability.
Active and Reactive Power Control based on Predictive Voltage Control in a Six-Phase Generation System using Modular Matrix Converters

Renewable energy generation systems under distributed generation frame emerges as a plausible solution for nowadays growing world energy demands. In this context multiphase wind generation systems are a feasible option that consist of renewable AC source that need efficient and totally controlled power conversion stages. In this work a novel active and reactive power control strategy based on two cascade control loops using a combination of classical PR controller and Model Based Predictive Voltage Control is proposed. Furthermore, the generator is a Permanent Magnet Synchronous Generator and the power stage is based on a multi-modular direct matrix converter topology providing interesting features to the scheme. The performance of the whole system is analysed regarding tracking of reference and THD with satisfying transient results and THD lower than 1.52 % in the injected current widely accomplishing with international standards.
Towards Cascading Problem for Dynamic Rate Allocations in ISP Networks with SDN

To improve the experience of various network applications, dynamic rate allocation is an essential issue in recent ISP networks. The emergence of software-defined networking (SDN) and the OpenFlow specification makes dynamic rate allocation in ISP networks efficient. The allocation could locally run on a home network gateway (edge switch) under SDN, but such local range of rate allocation reduces the overall fairness and performance in the whole network. However, under a global range, the request of rate allocation from a small number of hosts will cause all switches on the entire network to participate. This is termed as cascading problem, which causes a high cost for re-allocating the rates with the global range of switches. In this paper, we investigate the cascading problem for dynamic rate allocation with SDN, and discuss the tradeoff between the performance and cost for the range of rate allocation. We propose a Rate Allocation algorithm with Limited Range (RALR) in SDN, and discuss it for dynamic rate allocation by the theory of Lyapunov drift. Our intensive simulations verify the performance of the proposed strategy of rate allocation in SDN.
Pedestrian Flow Prediction with Business Events

Pedestrian flow is an important indicator of public places, since it can provide more potential economic benefits. Pedestrian flow prediction is developed to help the decisionmaking for the operators (such as shopping center owner). Furthermore, the operators aperiodically arrange some events to attract the nearby pedestrians, such as the sales promotions, and we term this kind of events as business event. Moreover, their placement will affect the distributions of the pedestrian flows. In this paper, we investigate the influence of the business events on the pedestrian flows. Then, we propose an Attraction Based Matrix Factorization model, called ABMF, to efficiently predict the pedestrian flow with business events and enable operators to formulate candidate solutions. The experimental results show the superiority of our prediction method compared with other state-of-the-art prediction techniques.
Nanometer Precision With a Planar Parallel Continuum Robot

In many cases, soft and continuum robots represent an interesting alternative to articulated robots because they have the advantages of miniaturization capability, safer interactions with humans and often simpler fabricating and integration. However, these benefits are usually considered to arise at the expense of accuracy and precision because of the soft or flexible limbs. This paper demonstrates that, with a proper design, a planar parallel continuum robot is capable of great precision. Indeed, the proposed 3-Degrees-of-Freedom planar parallel continuum robot exhibits a precision of 9.13 nm in position and 1.2 μrad in orientation. In addition, the novel robotic design leverages the effect of the actuators’ defects, making the robot more precise than its own actuators. Finally, the workspace of the proposed robot (62.3 mm$^2$, 0.6452 rad) is significantly larger than most compliant mechanisms, which is particularly interesting when both very high precision and relatively large displacements are required.
High Power Factor Rectifier Using One Cycle Control Strategy

Due to the considerable increase in the insertion of electronic components to Electric Power Systems (EPSs), the present article analyzes a method to eliminate undesired currents originated by nonlinear loads connected to the grid and further performs the Power Factor (PF) correction on single-phase rectifier loads. The proposed solution uses the topology of the DC-DC boost converter working in the Boundary Conduction Mode (BCM) as a Power Factor Correction (PFC) circuit and implemented using the One Cycle Control (OCC) strategy, which proposes high efficiency during the switching period. The simulations yielded interesting results regarding the proposed control method, with the results showing the increase of the PF and reduction of the source current Total Harmonic Distortion (THD), benefiting the power quality of the system.
A Collaboration Robot Arm Platform based on Integration Joints for Manufacturing and Medical

An aluminum-links collaboration manipulator based on the Kollmorgen RGM joint and embedded control system is presented in this paper. Because of its low inertia and reduction ratio, this manipulator can be used for human-robot collaboration. The purpose is solving the problem of human security and the operability in human-robot collaboration environment. CANopen bus is used and obtains a steady control period up to 2.5ms. As for motion planning layers, a PC platform using Robot Operating System(ROS) send trajectory points to our manipulator controller. Finally, the motion interpolation and trajectory closed-loop control algorithm is designed, and a physical trajectory tracking experiment of the Kollmorgen manipulator is done, and obtained trajectory error within 3 percent.
A Carrier-Based Modulation With Planned Zero Sequence Voltage Injection to Control Neutral Point Voltage for Three-Level Inverter

In this paper, a carrier-based modulation strategy with planned zero sequence voltage (ZSV) injection for T-type three-level inverter (TLI) is proposed to control neutral point (NP) voltage, which is simple to be implemented. This strategy is analyzed comprehensively based on the relationship between NP current and the injected ZSV in one switching cycle. The planned ZSV is calculated according to the voltage difference between upper and lower capacitors and the injectable ZSV range without over-modulation. Furthermore, in order to reduce switching losses, the closest clamping mode method is applied, in which specific ZSV is injected to form discontinuous pulse width modulation (DPWM), and the NP voltage control ability is sacrificed to some extent. In most applications of the two proposed methods, the NP voltage can be perfectly controlled almost like virtual space vector PWM (VSVPWM). However, the switching numbers of the strategy with planned ZSV injection are a third even half less than that of VSVPWM. And the switching numbers of the closest clamping mode method are even a third less than that of SVPWM. Comprehensive experiments are carried out to verify the feasibility of the proposed two methods.
Channel layer-dependent optimization of on-state current and subthreshold swing of MoS2 TFET

This paper presents a comparative analysis among two different layers (monolayer and multilayer) of molybdenum disulfide (MoS2) channel based n-type tunnel field-effect transistor (TFET) to optimize on-state current and subthreshold swing ($S_{th} S$) by tuning different device parameters such as oxide thickness, doping concentration, screening length, dielectric strength as a function of gate voltage and electric field. Two closed-form analytical models of tunneling current and ${S_{th}} S$ are used for MoS2-based TFET due to the superior performance of MoS2 as a channel material for post-silicon and beyond-graphene technology. Additionally, band-to-band tunneling (BTBT) mechanism results in higher on-state current and lower $S_{th} S$ in TFETs compared to MOSFETs. The present study reveals that it's quite hard to get the best-optimized results for the on-state current and $S_{th} S$ simultaneously, that is, there is a tradeoff between them. The whole optimization process is carried out by varying two parameters and variables concurrently in order to have more accurate results.
Cluster Density Properties Define a Graph for Effective Pattern Feature Selection

Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without significant or noticeable loss of information. The purpose of this study is to develop and investigate a new unsupervised feature selection method which uses the k-influence space concept and subspace learning to map features onto a weighted graph and rank them by importance according to the PageRank graph centrality measure. The graph design in this method promotes feature relevance, downgrades redundancy, and is robust to outliers and cluster imbalances. In K-Means classification experiments using the ASU feature selection testing datasets, the method produces better accuracy and normalized mutual information results than state-of-the-art unsupervised feature selection algorithms. In a further evaluation, using a dataset of over 14,000 tweets, conventional classification of features selected by the method gave better sentiment analysis results than deep learning feature selection and classification.
A Novel Configuration Tuning Method Based on Feature Selection for Hadoop MapReduce

Configuration parameter optimization is an important means of improving the performance of the MapReduce model. The existing parameter tuning methods usually optimize all configuration parameters in MapReduce. However, it is exceedingly challenging to tune all the parameters for the MapReduce model because there are massive configuration parameters in MapReduce. In this paper, a novel configuration parameter tuning method based on a feature selection algorithm is proposed, and it is composed of the feature selection objective function and feature selection process. The objective function is based on the kernel clustering algorithm, in which anisotropic Gaussian kernel is adopted instead of the traditional Gaussian kernel to accurately judge the importance of each parameter in MapReduce. Then, the relationship between the configuration parameters in MapReduce and the features in the feature selection algorithm is defined. Moreover, the importance of each parameter is reflected by the kernel width of anisotropic Gaussian kernels. At the same time, the method of gradient descent is introduced to update the kernel width and control the feature selection process of the iterative algorithm. Finally, experimental results show that the proposed algorithm performs suitably for the MapReduce model.
Colorectal Tumor Segmentation of CT Scans Based on a Convolutional Neural Network With an Attention Mechanism

Due to the irregularity of colorectal tumor contours, it is a challenging task for clinicians to segment colorectal tumors manually in CT scans. To solve this problem, a novel algorithm based on a convolutional neural network with an attention mechanism is proposed to automatically achieve tumor segmentation. The proposed network consists of three major modules: an encoder module, which is fed CT scans to attain the feature map; a dual attention module, which includes a channel attention module and a position attention module to obtain more contextual information in the deep layer of the network; and a decoder module, which restores the feature map to the original size of the input images. We used 1131 CT slices of colorectal tumors to train and test the proposed network. Compared with U-Net and CE-Net, the Dice coefficient increased by 1.46% and 0.66% respectively, for our model. The comprehensive results show that the proposed network performs more effectively in colorectal tumor segmentation than the other methods.
An Improved Stator Winding Short-circuit Fault Diagnosis using AdaBoost Algorithm

Brushless DC (BLDC) motors, bearing the characteristics of permanent magnet synchronous machines, have gained immense popularity in industrial applications due to its excellent efficiency and ease in control over conventional DC motors. Stator related faults are the most common types of faults in BLDC motors while operating under a higher loading and complex condition. Conventional machine learning (ML) classifiers such as- Support Vector Machines (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB) classifiers fail to obtain optimum accuracy when there are abrupt changes in health states of the BLDC motor. Especially the classification of weak health features ends up with an erroneous result. Boosting techniques allow adding weights to weak features and a better result in classification. This study presents an improved stator related fault classification of BLDC Motor using AdaBoost technique. Several parameters of AdaBoost algorithm are optimized and implemented along with Random Forest (RF) classifier in order to determine a developed algorithm for fault classification.
Differential Fiber Optic Gyroscope Driven by Two Broadband Sources of Different Wavelengths

We propose a differential fiber optic gyroscope driven by two broadband light sources with different wavelengths. Preliminary experiments demonstrate that both bias drift and angle random walk can be reduced considerably due to common-mode error cancellation. Theoretical analysis shows that the effect of transient temperature could be eliminated by optimizing the dispersion of the fiber coil and/or by introducing a dispersion compensation factor before taking the differential operation. Experiments with a fiber coil with weak wavelength-dependent dispersion coefficient show that significant reduction of bias drift due to transient temperature can be achieved with a proper dispersion compensation factor.
IoT Based Home Automation System with Customizable GUI and Low Cost Embedded System

This paper presents a customizable GUI and an inexpensive embedded system with internet connectivity for monitoring and controlling several devices and home appliances remotely, using android-based smart phone application or computer-based application. The system consists of a customizable GUI that facilitates the users' demand; micro-controller helps convey the user input to the system; wireless connection to the devices under the system. This study explains the overall design of a low cost Home Automation System (HAS) with wireless (WiFi) system (Internet). This HAS is designed to assist and provide support in order to fulfill the needs of children, elderly people and common disabled individuals in their home. In addition, the smart home concept based on IoT improves the standard of living at home. The main control system implements a client server relationship to provide remote access from smart phone through wireless Internet technology. The switches of the electrical appliances are synchronized with the entire control systems in a way that every user interface displays the real time status of the existing switches. Using EEPROM, the last status is preserved and in case of power cut, the system will retain the last-known reading when it recovers from a sudden calamity. The novelty of the system is it gives permission to multiple users at the same time to access the system and change their priority. This system is designed with customizable GUI, inexpensive embedded system, and it is easy to install, control and monitor with an array of electronic devices widely used in everyday home chores.
Neural Signal Blocking Based on Spike Trapping Principle: Effect of Electrode and Pulse Parameters

Electrical stimulation of nerves to treat neurological disorders has been extensively studied. In this paper, a proactive blocking method, spike trapping, is introduced, which was previously referred to as the “antimissile strategy”. Acute experiments on sciatic nerve–gastrocnemius muscle preparations of bullfrogs were performed to explore the parameters of this blocking method, including the size, spacing, and placement of the electrodes, as well as the widths of the cathodic triangular pulses used to trap the evoked neural signals. The block threshold voltage decreased with increasing electrode spacing and for larger diameter when the tripolar platinum wire electrodes were placed longitudinally on the same side of the sciatic nerve. However, there was no statistically significant difference between the minimum pulse widths which requires achieving nerve block with different electrode spacing or size. Placing bipolar electrodes of asymmetrical shape and size on the opposite sides across the nerve, the block threshold and the minimum pulse width both decreased first and then increased as a function of increasing anode width. Whether the electrodes is placed longitudinally along the nerve with tripolar configuration or transversely across the nerve with bipolar configuration, the nerve conduction can be instantaneously blocked without undesired excitation using the spike trapping strategy.
A Generalized Obfuscation Method to Protect Software of Mobile Apps

As the world is becoming more mobile, mobile applications (or apps) are an integral part of our everyday personal and professional lives. Despite their unprecedented utility, these apps can pose serious security risks since a lot of critical or sensitive information is contained in the distributed software. Therefore, preventing a legitimate software from malicious reverse engineering and other white-box attack is a challenging task. Code obfuscation is a commonly used method to protect software. However, most obfuscation methods merely make the control flow of the program complicated rather than hide the inner logic, and then they are often defeated by reverse engineering. In this paper, we present a new generalized approach to code obfuscation that aims at hiding the basic mathematical operations of the program. This approach splits the basic operations into a set of sub-operations that are replaced by the results retrieved from the protected lookup tables. In order to increase the difficulty for attack analysis, we design the random bijection method and structure similarity method to make the control flow of different obfuscated operation indistinguishable from each other. We also implement our proposed obfuscation method on both source code level and binary code level to demonstrate its broad applicability and examine the performance from multiple dimensions.
A Multi-Objective Genetic Algorithm Approach for Synergetic Source-Storage-Load Dispatch in a Residential Microgrid

The current work presented in this article proposes a day ahead multi-objective optimization portfolio for a residential microgrid or a home consisting of different domestic appliances and rooftop solar panels associated with storage device. The proposed framework aims to determine synergetic source-storage-load dispatch schedule by simultaneously minimizing the electricity cost, ocular discomfort and thermal discomfort experienced by the home occupants for the following day while controlling switching of the discrete loads (like washing machine, dryer etc.) and consumption of the continuous loads (like air-conditioner, lights etc.). The entire optimization framework takes the form of a mixed integer non-linear programming and the solution technique is proposed using Genetic Algorithm. The simulation is carried out on practical data set of a residential unit to prove effectiveness of the designed method.
A Language-independent gender classifier for Online Social Networks

Designing gender predictor for the Online Social Network (OSN) is receiving considerable attention from the research communities of different domains. However, the gender classifiers proposed by earlier studies for social media content so far, highly rely on the language used by the users for writing the content. It implies that the prediction model trained on one language (say English) will likely fail in identifying the gender of users with other languages (for example, Spanish). The study conducted in this paper aims to identify the features from user content on an OSN, which will assist in devising a Language-Independent Gender Classifier (LIGC). The experiments are performed on the Facebook networking site. The site provides the users with the list of various personal attributes that they may or may not reveal to other users on the network. The presented work collects such information of the Facebook users and carries out rigorous feature analysis to know whether this information varies between men and women on Facebook. Furthermore, several machine learning algorithms including Random Forest, SVM, Naïve Bayes, and kNN have been employed to determine the potential of the proposed feature set. Random forest approach achieves the highest value (70%) of performance metric, AUROC (Area under the Receiver Operating Characteristic). The current study is the first attempt to utilize information revelation for designing a gender identifier for OSN that is independent of the language used by the members.