Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotation-Free Online Handwritten Chinese Character Recognition using Two-Stage Convolutional Neural Network

Handwritten Chinese character recognition (HCCR) is a major research area in pattern recognition. Although recent years have seen tremendous improvement in the use of deep neural networks in handwriting recognition, unconstrained handwriting recognition remains an open problem. Considering the problem of rotated handwritten Chinese character recognition, we propose a two-stage convolutional neural network combined with path signature features to achieve high-accuracy rotation-free HCCR. For handwritten Chinese characters whose rotation angle is up to ±45°, the recognition rate can reach 97.38% on the ICDAR-2013 online HCCR competition dataset, which is comparable to the accuracy achieved by several state-of-the-art methods for non-rotated characters, thus showing the effectiveness of the proposed method.
Similarity Measures in Development of an Indoor Localization System

One of the issues faced by manufacturing industry is a lack of automatic localization techniques. In this research, a Radio Frequency Identification (RFID) based localization system is proposed for resource tracking. In this study, we incorporated a RFID tag at each item to be tracked, and a RFID reference tag at each location zone. The encoded IDs are read to identify the names of items and location zones. At the same time, radio signals (received signal strength and phase) are measured as RFID fingerprints. Similarity measures are studied to compare fingerprints between RFID item tags and location reference tags to track the location of the items. The kernel-based learning method was implemented as similarity measure. Different cluster labelling methods were compared and it was found that the proximity method is more efficient. The clustering method is used to overcome the issues faced by traditional RFID based localization methods.
Exploring Sparse Representation for Improved Online Handwriting Recognition

This work studies the sparse representation based classification (SRC) framework for online handwriting recognition (HR) task. In this framework, first, an exemplar dictionary is created using the training samples from each of the classes in the chosen task. Subsequently, the test samples are sparse coded over exemplar dictionary for classification. In sparse coding, both l_0 - and l_1 -norm based greedy algorithms are studied. Further, for reducing the computational cost of the SRC-based HR approach, the learned exemplar dictionary has also been explored. The proposed SRC-based approach is demonstrated for character and limited vocabulary word recognition task and evaluated on three different corpora: the Assamese digit database, the UNIPEN English character database and the UNIPEN ICROW-03 English word database. The experimental results are promising over the reported works on these databases employing the hidden Markov model or the support vector machine.
A parameter tuning approach of the Sliding Mode Control for a Quadcopter based on Genetic Algorithms

In this work is presented a parameter tuning approach of a Sliding Mode Control (SMC) using Genetic Algorithms (GA). The GA are used for the tuning of the parameters of the Orientation Control and Height of quadcopter, considering all the cases in a sliding surface PD. The objective of GA is to provide a set of parameters that minimizes the integral square error (ISE) of the SMC. The SMC is improved when it is used the GA.
Current Limiting Protection Scheme for Multi-Terminal DC Grids Based on Transient Boundary Voltage

Multi-terminal DC grids based on voltage source converters (VSCs) are widely recognized as a feasible solution to cope with increasing electricity demand and high penetration of new renewable energy sources. The VSCs are vulnerable to DC line faults due to the high discharge current of DC-link capacitance. The high current would damage the insulation of DC cable, and subsequently destroy the anti-parallel diodes during freewheeling stage. In this paper, a current limiting protection scheme, which includes a current limiting circuit and a fault detection method based transient boundary voltage, for multi-terminal DC grids. The current limiting circuit is proposed to suppress the fault current within the interrupting capability of hybrid DC circuit breakers (HCB). The fault detection method is proposed to achieve fast and reliable fault detection of DC line. Comparisons with some conventional protection methods reveal the promising performance of proposed fault detection method. Simulation results of several cases have been verified the effectiveness of the proposed protection scheme, and indicate that the faulted DC line can be identified correctly and quickly, and the non-fault system is able to recover to normal operation immediately after fault clearance without power interruption.
Data Provenance With Retention of Reference Relations

With the development of data transactions, data security issues have become increasingly important. For example, the copyright authentication and provenance of data have become the primary requirements for data security defense mechanisms. For this purpose, this paper proposes a data provenance system with retention of reference relations (called RRDP), which can enhance the security of data service in the process of publishing and transmission. The system model for data provenance with retention of reference relations adds virtual primary keys using reference relations between data tables. Traditional provenance algorithms have limitations on data types. This model has no such limitations. Added primary key is auto-incrementing integer number. Multi-level encryption is performed on the data watermarking to ensure the secure distribution of data. The experimental results show that the data provenance system with retention of reference relations has good accuracy and robustness of the provenance about common database attacks.
Robust Skeleton-based Action Recognition through Hierarchical Aggregation of Local and Global Spatio-temporal Features

Recognizing human actions based on 3D skeleton data, commonly referred to as 3D action recognition, is fast gaining interest from the scientific community recently, because this approach presents a robust, compact and a perspective-invariant representation of motion data. Recent attempts on this problem proposed the development of RNN-based learning methods to model the temporal dependency in the sequential data. In this paper, we extend this idea to a hierarchical spatio-temporal domains to exploit the local and global features embedded in the long skeleton sequence. We introduce a novel temporal-contextual recurrent layer to learn the local features from consecutive frames and then to aggregate the extracted features hierarchically, refining the sequence representation layer by layer. Our method achieves competitive performance on 3 popular benchmark datasets for 3D human action analysis.
Dynamic Compliance Analysis for LHDS of Legged Robot, Part B: Force-Based Impedance Control

In this paper, the dynamic compliance analysis for force-based impedance control is studied. First, the mathematical model of legged robot joint hydraulic driver, called hydraulic drive unit (HDU), is built. Then the force-based impedance control model of the leg hydraulic drive system (LHDS) is built based on the robot system kinematics, statics and dynamics. Next the dynamic compliance of the control model is studied and found to be composed in a serial-parallel structure. Further, the two main factors which influence the impedance control accuracy are found. Finally, the composition theory of dynamic compliance is proved by experiments. The theory proposed in this paper provide theoretical foundation for the compensation of force-based impedance control.
Services KPI-based Energy Management Strategies for Green Wireless Networks

Complex green wireless networks including smart grid, renewable energy and batteries need precise energy management strategies in order to realize some of the defined objectives. Although these networks have different services (streaming, web, voice call, etc.) with different requirements, most research work slightly covered the specific contribution of each of these services and their effect on the network energy consumption. In this paper, we consider advanced KPIs (Key Performance Indicators) putting forward each service contribution to energy consumption. Using these new KPIs, we propose to adapt some energy management strategies, leading to performance amelioration and new energy savings, under renewable energy and smart grid environment. Simulations indicate the efficiency of our proposed adaptation, achieving 11.45% enhancement in these new KPIs, and outperforming several benchmark algorithms with a gain up to 8% points in terms of energy savings reduction. We analyze the benefits of the services KPIs and also their limits, leading to possible new services-based efficient energy management strategies in the considered framework.
Data Preprocessing for Soft Sensor Using Generative Adversarial Networks

Soft sensors have been widely used in industrial processes in the past two decades, using easy-to-measure process variables to predict hard-to-measure ones. Sufficient training data can significantly improve the prediction performance as well as speed up the computation with low cost in the modeling process of the soft sensor. However, data collection is often difficult due to the harsh environment of the industrial process. Generative adversarial networks (GANs) is a prominent method for learning generative models in recent years. In this paper, a generative model named DWGAN based on improved Wasserstein generative adversarial networks (WGAN) is proposed to generate new samples for soft sensors. For evaluating the proposed method, a practical industrial experiment is studied in the case of paucity of data, and three other state-of-the-art generative models are adopted for comparison. The experimental results show that the samples yielded by the proposed method behave more similarly with the real samples compared to samples provided by other methods. Additionally, these synthetic samples can make the training data sufficient and significantly improve the prediction accuracy of the soft sensors.
ICFHR 2018 Competition on Recognition of Historical Arabic Scientific Manuscripts – RASM2018

This paper presents an objective comparative evaluation of page analysis and recognition methods for historical scientific manuscripts with text in Arabic language and script. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICFHR2018, presenting the results of the evaluation of six methods – three submitted and three baseline systems. The challenges for the participants included page segmentation, text line detection, and optical character recognition (OCR). Different evaluation metrics were used to gain an insight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that, despite the challenging nature of the material, useful digitisation outputs can be produced.
The Generalized Contrast-to-Noise Ratio

Many adaptive algorithms claim to provide higher contrast than delay-and-sum (DAS). These claims are often backed by estimations of the contrast-to-noise ratio (CNR). Intuitively, we assume that higher CNR leads to higher probability of lesion detection, and this is indeed the case for DAS. However, non-linear processing can arbitrarily alter CNR, and yet yield no improvement in the detection probability. We propose a new image quality index, the generalized contrast-to-noise ratio (GCNR), based on the overlap area of the probability density function inside and outside the target area. GCNR can be used with non-linear beamforming algorithms, remaining unaltered if the dynamic range is changed. We demonstrate that GCNR is proportional to the maximum success rate that can be expected from the algorithm. Using Field II, we compare the performance of CNR and GCNR in 6 imaging algorithms. While CNR varies significantly between the 6 algorithms, we do not observe notable variations in GCNR (<10%), which means that the 6 algorithms have similar lesion detection capabilities. GCNR fixes the methodological flaw of using CNR with algorithms that alter the probability density function of the ultrasound signal, and allows us to assess the significance of contrast enhancing effects in ultrasound imaging.
A Learning-based Approach for Error Compensation of Industrial Manipulator with Hybrid Model

The industrial robot usually has high repeatability but relatively lower accuracy. Therefore, error compensation plays a pivotal role in many industrial robotic applications with high accuracy requirement. In this paper, we present a novel computational method that utilizes a hybrid model that consists of Local Product-Of-Exponential (POE) and Gaussian Process Regression (GPR) to compensate the positioning errors of the industrial robotic manipulator for high accuracy industrial robotic applications. Specifically in the proposed method, the Local POE calibration method is first applied to calibrate the robot forward kinematic model to reduce the geometric error. Then the GPR is applied to learn the inverse kinematic model to further compensate the residual error in task space. We also demonstrate the robustness and effectiveness of our proposed method by showing the reduction of norm pose error by up to 37.2%, compared to the existing methods with multiple datasets.
Leveraging Named Data Networking for Fragmented Networks in Smart Metropolitan Cities

A fragmented network is a phenomenon caused when a metropolitan area network is affected due to some natural calamities such as fire, earthquake, flood, and so on. A reliable communication among nodes in the fragmented network is a vital research issue. Different solutions have been proposed to establish communications among nodes in such a fragmented network. One such solution is to make the use of named data networking (NDN). The NDN with the help of content store, pending interest table, and forwarding information base, establishes the communication with other nodes based on the names of contents, rather than the nodes themselves. However, these solutions are inefficient in the case of fragmented networks. In this research, we propose an NDN-based technique which uses the satisfied interest table (SIT) along with the push-based special alert message to communicate with other nodes to overcome such issues. We also use the Nonce phenomena to avoid loops during communications. The use of SIT helps a node to find the relative information quickly in the fragmented networks. By using this technique along with the SAM, we achieved better efficiency and response time.
Radiation Performance of a Flash NOR Device

We present the results of single-event effects (SEE) and total ionizing-dose (TID) testing performed on the die used in DDC's 56F64008 flash-NOR devices. The device was single event latchup (SEL) immune at LET=85 MeV cm2/mg. All single event functional interrupts (SEFI) observed could be cleared by resetting the part without a need for power cycling.
Mining Location Influence for Location Promotion in Location-Based Social Networks

With the extensive application and in-depth study of location-based social networks (LBSNs), more and more businesses are utilizing the new social platform to promote their products and services. In this paper, we explore an important technique that can help business to promote their locations by distributing items with business information or the leaflets of the business at some locations. In order to maximize the benefit of location promotion, this paper formally defines it as a location influence scope maximization problem on an location-based social network, i.e., given a target business and an LBSN, the problem is to find a set of $K$ locations, where the business distributes small promotional gifts or leaflets on, such that people who visited these locations can successfully propagate the advertisement information to most other locations for attracting most people to visit the target location. The existing related researches neglect location influences studies on location promotion (outdoor marketing). And it is more challenging to derive the influence between locations and the spatial influence scope of each location, since location influence depends on users mobility and the target location. In this paper, we provide a new approach (called as LoP) to exploit influence between locations for location promotion. First, LoP incrementally mines correlations between locations to instruct the location-location graph (LLG). Then, LoP predicts each location’s influence on others and its influence scope based on betweenness centrality. Finally, we provide the lazy algorithm to efficiently find a top-K set of influential locations for the business. Extensive experiments based on two real LBSN datasets have verified the superior effectiveness of our proposed method in term of location promotion.
Folded-Line Left Handed Metamaterial Enhanced Microstrip Patch Antenna

The design, simulation and fabrication of a Folded-Line Left Handed Metamaterial (FL-LHM) is discussed. The FL-LHM that consists of Split Ring Resonators (SRRs) are modified and described using LC lump elements analysis to obtain the negative value of permittivity and permeability. Nicolson-Ross-Wier (NRW) approach is used to identify the double negative region from the scattering parameter of FL-LHM unit cell. FL-LHM structure is applied to the antenna to improve the gain of microstrip patch antenna. The measurement results matched well with that of the simulation. The integration of FL-LHM and microstrip antennas has enhanced the gain nearly 2 dB and the side lobe suppression is shown in the radiation pattern. In addition, we also able to improve the gain of antenna without sacrificing its dimension when using FL-LHM. These improvements are made possible due to the characteristics of the negative metamaterial of the LHM structure that allows the EM waves to propagate and refocus when placed at front of the antennas.
Reduce Redundancies: Signal-based Clustering of Large-scale Fingerprint Data

For Bluetooth- or WIFI-based localization, fingerprinting data plays an important role. Mobile devices compare their received signals with recorded signals on a map (so-called reference points) and derive their most like location from that. Obviously, this method requires an elaborate offline phase to record the reference points. If this data set is too small and not dense enough, the localization accuracy is low, too. However, by just increasing the number of recorded data points, storage and comparison costs on the mobile device are also increased. Hence, the goal of this work is to find the best reference points within large-scale sets of fingerprinting data based on clustering. We present different novel algorithms for signal-based clustering and compare them with existing work. An extensive evaluation on real-world data sets shows that our approach can reduce the data set size up to 90% while keeping a mean accuracy of 1.0m in the experiments in the real world.
Distributed State Estimation for Smart Grids Considering Packet Dropouts

This paper develops a distributed state estimation approach for smart grids. Particularly, the designed filter is developed in an interconnected way where the packet dropouts take place between estimators. The error function between true and estimated states is written in compact form, then it can be transformed into the linear matrix inequality (LMI). After solving the LMI problem, the desired gains are determined for the smart grid state estimation. The proposed method is applied to the IEEE 14-bus system where system state and input matrices are obtained from the Holt-Winters approach.
Typical Scenario Reactive Power Optimization of Active Distribution Network Based on Limit Theory

With the power output of new energy farm in active distribution network is highly uncertain, the forecast error may lead to the insufficient reactive power capacity of the power grid when scheduling mechanism formulates the scheduling strategy according to the predctive power of new energy farm. The limit theory is introduced to establish the relationship between the active output power and the reactive power range of new energy farm. The power output of new energy is divide into different typical scenario based on the K_means clustering method, and the probability of each scenario is determined according to the historical data. The adjustable capacity range of new energy reactive power under each scenario is determined based on the limit theory. A typical reactive power optimization model, which considers the uncertainty, is established. The simulation result obtained by a actual 48 nodes network show that the proposed method can more accurately determine the reactive power range, and it can reduce the power loss of network and make the voltage level reasonable when regarding the new energy farm as a reactive power source.
A Machine Learning Approach for Tuning Model Predictive Controllers

Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.
An Improved PSO-Based Power Allocation Algorithm for the Optimal EE and SE Tradeoff in Downlink NOMA Systems

Non-orthogonal multiple access (NOMA) technique can substantially improve the spectral efficiency (SE) and increase the throughput of system. As NOMA has these advantages, it has been investigated as a candidate radio access technology for the fifth-generation (5G) mobile communication. In this paper, we study the benefit of NOMA in enhancing energy efficiency (EE) for downlink systems by introducing a certain power allocation scheme. The expected goal is to maximize the EE of the systems while ensuring the SE is guaranteed. The most common method for achieving the tradeoff of EE and SE is the convex optimization. Because the EE of NOMA systems is a strictly quasi-concave function, it is difficult to apply the convex optimization directly. We propose to adopt the improved particle swarm optimization (PSO) algorithm. Furthermore, in order to avoid the premature convergence, cycle strategy rotation is used in the algorithm. Numerical results and analysis turn out that the improved PSO algorithm achieves higher EE than the exiting power allocation scheme under the same SE. Moreover, the improved PSO algorithm shows fast convergence and it converges within 9 iterations, which is 35% less than the original PSO algorithm under selected parameters.