Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Backstepping Control of Uncertain Gear Transmission Servosystems With Asymmetric Dead-Zone Nonlinearity

In this paper, an adaptive backstepping control scheme is presented to solve the position control problem for a gear transmission servosystem with unknown asymmetric sandwiched dead-zone nonlinearity. All the parameters involved in both the system and dead-zone models are allowed to be totally unknown. In contrast to the previous results where the effects of symmetric dead zone can be treated as additive disturbances in a transformed system, most of the unknown parameters of the transformed system are now time varying and dependent on the real-time value of relative displacement between the driving and driven gears. To deal with the above issue, new compensating terms in the quadratic form are introduced in the design of the virtual control signal for each recursive step. With the adaptive laws modified correspondingly, we can still show that all the closed-loop signals are ensured to be bounded and the output regulation error will converge to a compact set. Simulation results are provided to show the effectiveness of the proposed adaptive control scheme.
Improved Epoch Extraction Using Variational Mode Decomposition Based Spectral Smoothing of Zero Frequency Filtered Emotive Speech Signals

The objective of the present work is to improve the epoch extraction performance from emotive speech by proposing a post processing approach to the conventional zero frequency filtering (ZFF) method using variational mode decomposition (VMD) based spectral smoothing. Due to the fast uncontrolled variations of the pitch in emotive speech signals, the reliable estimation of epochs is always challenging. In the proposed method, the spectra of the short frames of zero frequency filtered signal (ZFFS) is subjected variational mode decomposition to get component spectra in five modes. A smoothed short time spectra is then obtained by excluding the spectra from the two higher VMD modes which essentially have the high spectral variations. The modified ZFFS is then reconstructed using the sinusoidal parameters corresponding to single dominant frequency present in the smoothed spectra using VMD by parameter interpolation based sinusoidal synthesis. The resulting re-synthesized ZFFS has reduced spurious zero crossings as compared to that obtained from the conventional ZFF method for emotive speech signals. The effectiveness of the proposed VMD based spectral post processing is confirmed from the improved epoch identification rate and epoch identification accuracy across all the emotive utterances (with 7 emotions) present in German emotion speech database having simultaneous speech and electroglottographic (EGG) signal recordings. The performance of the proposed method is found to be better or comparable with the other existing ZFF based post processing methods proposed for emotive speech signals in terms of the epoch identification accuracy with respect to the corresponding reference epochs estimated from EGG signals.
An Automatic Impedance Matching Method Based on the Feedforward-Backpropagation Neural Network for a WPT System

In a wireless power transfer (WPT) system via coupled magnetic resonances, the power transfer efficiency (PTE) drastically decreases with the transfer distance or the load changing. In this paper, the causes of efficiency degradation are analyzed, and an automatic impedance matching method based on the feedforward-backpropagation (BP) neural network is proposed to maintain the PTE at a reasonable level. To validate and test the performance of the proposed method, a WPT automatic impedance matching simulation system is implemented. Moreover, a prototype based on the proposed method is built and dynamic matching experiments were performed. The simulation results show that the algorithm efficiency of the proposed BP method is 108.5% higher than that of the genetic algorithm. The experimental results show that the PTE is improved up to 78.33% and this is closely maintained within a distance of 10–30 cm, which is consistent with the simulation result.
Amharic Character Image Recognition

In this paper we introduce Convolutional Neural Network (CNN) based method for Amharic character image recognition. We also introduce a dataset for training purposes. The proposed method has less pre-processing steps and out per- forms the state-of-the-art by a large margin. Experiments were done on 80,000 Amharic character images which was generated with different degradation level. We systematically evaluated the performance of the recognition model and achieved the state-of- art performance with an average recognition accuracy of 92.71%.
Efficient Transactive Control for Energy Storage Management System in Prosumer-Centric Networked Microgrids

This paper presents a transactive control (TC) mechanism for the management of battery energy storage systems (BESS) in residential networked microgrids (MGs) that contain loads, electric vehicles (EVs), and rooftop solar photovoltaic systems (PV). The goals of the TC are to maximize the savings of consumers and prosumers and to reduce peak load on local transformers. This is accomplished by utilizing local hybrid PV-BESS resources from prosumer community groups (PCGs), which are scheduled to offset peak loads. A model predictive control (MPC) based method is utilized to optimize the BESS scheduling. In the proposed TC, the PCGs are incentivized by the distribution system operator (DSO) through a dynamic price signal that is being updated hourly based on the MG local conditions. To evaluate the proposed TC, case studies are conducted on residential MGs located in an IEEE 33-bus test system. The evaluation indicates that the proposed TC can improve the savings of prosumers/consumers, reduce peak demand caused by EV charging in the distribution networks, and is able to alleviate undesired grid effects, e.g., transformer overloads.
PWM Sensor-less Balancing Technique for the Fifteen-Level PUC Converter

In this paper, a novel PWM technique insuring the self-balancing of the capacitors voltages has been proposed for the attained nine-level packed $\mathbf{U}$ cells (PUC) converter. In the traditional fifteen-level operation, the capacitors voltages have to be maintained around their references using a state variable feedback which requires three sensors, two for the capacitors voltages and one for the load current. By applying the proposed PWM technique to the fifteen-level PUC converter, self-balancing of the capacitors voltages is attained, which results in the proposed nine-level converter. The proposed balancing technique is achieved without any closed loop regulation or sensors. The proposed concept has been verified by mean of simulations performed in the Matlab Simulink environments. Simulation results show that even under severe DC bus voltage and load parameters variations, the capacitors voltages remains around their required values. Harmonics contents of load current depend on the modulating signal frequency. Moreover, even under low frequencies, the total harmonics distortion remains reduced.
GMSK Viterbi Demodulation for Satellite-AIS

This paper presents a novel Viterbi method based on one-bit differential detection for Gaussian minimum shift keying (GMSK) used in the satellite based automatic identification system (AIS), which is easy to achieve in hardware. Because of the inherent intersymbol interference (ISI) of the GMSK, the bit error rate (BER) of hard decision based demodulation methods is always seriously exacerbated by Gaussian white noise especially under a small BT value. Correlation based traditional Viterbi method for GMSK will consume a mass of multiplier and adder resources and is not suitable for the hardware achievement. A phase rotation based differential Viterbi method is proposed in this paper, which just consumes few calculation resources and has better BER performance comparing with the hard decision method. The corresponding comparable result of the BER is also shown.
Analysis of Combined Z-Source Boost DC-DC Converter for Distributed Generation Systems

Paper proposes a combined Z-Source boost DC-DC converter for distributed generation systems. The traditional Z-source networks are limited to providing a boosted output voltage. Although by combining some Z-source network in different ways, its boost capability can be increased. The proposed converter can improve large range of output DC voltage and improve reliability with high voltage step-up capability. The proposed converter can be used in fuel cells and PV applications. The principle and comparison for the proposed converter topologies are presented in this paper. Simulation results depending upon the given duty ratio has been carried out using different operating conditions.
Augmented Handwritten Devanagari Digit Recognition Using Convolutional Autoencoder

Handwritten digit recognition has turned into one of the demanding areas of research in the field of image processing. Many approaches have been proposed which include a statistical method, fuzzy technique, and neural network for feature classification and feature selection but have not been found to use convolutional autoencoder for Devanagari digit after performing image augmentation on the training dataset. This paper shows the use of unsupervised training using convolutional autoencoder with deep ConvNet in order to detect handwritten Devanagari digits, i.e., 0–9. Convolutional autoencoder is the type of autoencoder that is used to encode the input for extracting important features and then try to reconstruct the input image. This paper shows the improved accuracy of Hindi, English and Bangla digit dataset by using the proposed approach and also performing a number of cross-validation experiments on all three datasets using image augmentation.
Performance Estimation of Scalable e-Learning Systems in the Cloud

Performance estimation of large scale cloud deployments of web servers is an ongoing challenge for researchers and operations engineers all over the globe. In this paper we propose an approach for performance estimation of cloud environment based on performance metrics and scalable coefficient between number of backend nodes and number of load balancers. An example performance estimation using this methodology is provided for a large automated environment in a public cloud.
Simultaneous Wireless Power and Bidirectional Information Transmission With a Single-Coil, Dual-Resonant Structure

Simultaneous wireless power and information transmission constitute an essential topic in the area of wireless power transfer (WPT) systems. In this paper, a dual-channel transmission model is constructed for integrating bidirectional data communication into a WPT system based on a single-coil, dual-resonant structure. Formulas for the Q value at the energy transmission channel, the bandwidth at the signal transmission channel, and the bidirectional crosstalk between the energy and signal transmission channels are deduced. The design methods for circuit parameters are analyzed with the aim of balancing the transmission power, signal transmission bandwidth, and crosstalk. Additionally, bidirectional communication transmission abilities with different coil misalignments are analyzed. Finally, three sets of different simulation models and an experimental platform are constructed to verify the proposed circuit design method and to implement an experimental system that transfers 354 W and 19.2 kbps with an air gap of 100 cm.
Automatically Answering Questions With Nature Languages

With the development of information technology, it becomes more and more difficult to retrieve information from the internet for users. Question Answering (QA) is one of the methods to solve this problem. The users type natural language questions and get answers in QA systems. However, most QA systems only return a word or several words to the user, which is not friendly enough. The users are more willing to receive not only answers but also additional introductions or reasons. In this work, we propose a Nature Language Question Answering system which utilizes Seq2Seq model and Generative Adversarial Network (GAN) to generate answers with more information for users. To our best knowledge, this is the first work generating natural language answers in Question Answering domain. Our experiment results show NLQA can generate readable answers for users.
Optimization of Capacity Allocation Problem in 4G LTE Mobile Networks

In today's information era, the mobile communication has outperformed as one of the leading technology for information exchange. It is highly possible, by mobile networks to provide any time and any where communication strategy, which meets the user requirements. However, the rising demand for multimedia, interactive and high quality of services on mobile network platform has made the existing mobile technologies to be reconsidered and reengineered. The 4G technology is one such platform that addresses the challenges in conventional mobile communication network. In this regard, 4G Long Term Evolution (4G -LTE) has promised to be effective solution to meet the future challenge of dynamism in all dimension of mobile communication. This work primarily focuses on reviewing the LTE mobile technology, the various challenges in designing LTE network and the issues in LTE while performing intended operations. Specifically, in LTE network design, the optimization of capacity allocation in LTE backhaul network is one of the most challenging issues due to high dynamic nature of mobile network communications and components. Such optimization problems are hard to solve in polynomial time. Next, this work addresses the reduction of LTE capacity allocation problem as NP complete using subset sum problem. Further, adaptive iterative local search algorithm is proposed to solve this problem. The experimental results exhibited the efficiency of proposed algorithm as 67% improvement in computation time in comparison of greedy method.
Analysis of the Health Status of Railway Vehicle Bearings Based on Improved HDP-HMM

In order to solve the problem that state number of the hidden Markov model (HMM) must be specified in advance and the convergence result of HDP-HMM is sensitive to hyperparameters, a bearing health status analysis model based on improved HDP-HMM is proposed in this paper. Based on the Hierarchical Dirichlet Process (HDP) and Hidden Markov Models, the model uses the nonparametric properties of the hierarchical Dirichlet process to infer the number of hidden states, compensates for the defects of HMM, and utilizes Bayesian Optimization and Mann-Kendall criteria optimize its hyperparameters. At the same time, considering the ergodic topology of the traditional HDP-HMM is not suitable for the timing monitoring data of the bearings, we convert the HDPHMM topology into a left-to-right mode, which is more suitable for the needs of health status analysis. In addition, taking the nonlinear characteristics of the performance degradation process of railway vehicle bearings into consideration, we use GKPCA (greedy kernel principal components analysis) to extract features of bearing degradation. Finally, the model is verified by using the monitoring data collected during the train running such as the bearing temperatures. The experimental results show that the proposed HDP-HMM can effectively identify multiple health status of the railway vehicle bearings and has reliable performance. It provides an important basis for the state repair of the railway vehicles bearings under actual conditions.
Non-stationary Events Detection Based on Extrema Value Theory

A detection of anomaly events in a non-stationary time series resembling vibration aspects is often based on the signal envelope. Both commonly used approaches, i.e. the Hilbert transform and the empirical mode decomposition, lead to high processing power requirements. As anomalies are assumed to be rare, we propose simplified methods based on extreme value theory that enable trend corrections and anomaly event occurrence detection. Both methods are based on the upper and lower envelope approximations that are constructed using maxima and minima limits of the signal.
Estimation Population Density Built on Multilayer Convolutional Neural Network

Automatic population density estimation is a significant study area in intelligent video monitoring. Traditional methods need design features manually, which are hard to keep pace with the current state of big data. At the same time, with the outbreak of artificial intelligence methods such as deep learning, the application of deep learning to video monitoring is also an irresistible trend. Therefore, according to the disadvantage of traditional manual feature extraction and the deficiency of single-layer convolutional neural network(CNN), a multilayer convolutional neural network(MCNN) is raised. In this article, head size changes caused by various reasons, such as penetration effect, will not affect the characteristics of CNN learning pictures. That is to say, even if we do not know the perspective of the input map, we can accurately detect the population density on the basis of adaptive kernel. The characteristic graphs of each layer are integrated to obtain the population density map. experiments reveals that this network structure can attain more accurate population estimation.
Risk Pricing and Consumers’ Equilibrium Strategies in A Quality-Based Make-to-Order/Service System

This paper is concentrated on the service provider’s optimal risky decision making problems in a quality-based make-to-order/service system. Different from a traditional operational management, consumers in our model have their own willingness to wait or leave when procuring service. We derive the risking pricing strategies under the interactions between service quality and consumers’ intensity. Making the quality and service speed trade-off, the optimal pricing strategies and optimal service rate are obtained for the monopoly provider and the social planner respectively. We also find how the service provider’s risk coefficient affect its optimal strategies as well as the consumers’ equilibrium strategies through numerical examples.
Onto-SB: Human Profile Ontology for Energy Efficiency in Smart Building

Ontology is considered one of the best tools for representation knowledge of a domain. It also facilitates the interchange of information and the interaction between systems. Although there is a lot of ontology’s in smart building area to resolve energy efficiency and other issues, we note the absence of essential concepts in existing ontology’s. The miss of some user profile concepts may lead to produce an unfavorable result and lead to a negative impact on user comfort. The user comfort is an important factor to evaluate the energy efficiency techniques in a smart building. In this work, we aim to present a smart building ontology supporting the human profile concept. This ontology takes into consideration maximum concepts associated with smart building and human profile. Existing applications and ontology’s related to the smart building and user profiling domain have been taken into account in order to create a general, comprehensive and extensible ontology. We will exploit human profile in reasoning to provide the adequate services in the smart buildings, maximize the inhabitant comfort, care out about human conditions and satisfy those needs. We will use protege 5 to implement our ontology using the OWL (Semantic Web language), and SWRL (Semantic Web Rule Language) for energy efficiency reasoning.
LoTTA: Energy-Efficient Processor for Always-On Applications

Various use cases in the era of Internet-of-Things (IoT) demand processor devices to have low energy consumption in order to maximize the battery life. In addition to energy constraints, there is often a need to both swiftly execute control-oriented code to provide low reaction times and to occasionally perform real time signal processing tasks efficiently. As a response to these requirements, we propose LoTTA, an extremely energy-efficient exposed datapath core. Its transport-triggered programming model helps in lowering the execution latency via low cost data forwarding. Control efficiency is achieved by an optimized control unit with zero delay slot branches and predicated execution. An instruction register file is included for frequently executed program hot spots to reduce the instruction stream energy consumption. These features allow the processor to execute CHStone and EEMBC CoreMark benchmarks on average with 19% fewer cycles compared to a 6-stage LM32, a traditional RISC core with similar datapath resources. The core consumes 53% less energy on average compared to the RISC core. When including the instruction stream overheads, in the best case, LoTTA saves 79% energy, and on average 40%.
Throat Microphone Speech Enhancement via Progressive Learning of Spectral Mapping Based on LSTM-RNN

In this paper, we propose a progressive spectral mapping learning algorithm for throat microphone (TM) speech enhancement. Unlike previous full-band spectra mapping algorithms, this algorithm divides the spectra mapping from TM speech to Air-conducted (AC) speech into two tasks, one is the voice conversion task, and the other is the artificial bandwidth extension task. Long short-term memory recurrent neural network (LSTM-RNN) is further deployed as the mapping model. Objective evaluation results show that the TM speech quality is improved when compared with conventional full-band spectra mapping framework and DNN-based mapping model.
Exploiting Class Hierarchies for Large-Scale Scene Classification Using Hybrid Discriminative Approach

Humans seamlessly perceive a massive amount of information while observing a scene. Though humans recognize real-world scenes easily and accurately but its not the same for computers due to scene images variability, ambiguity, and diverse illumination and scale conditions. Scene classification is a fundamental problem which provides contextual information to guide other processes, such as browsing, content-based image retrieval and object recognition. A baseline model based on traditional bag of words model is built to better evaluate the proposed solution. Model based on the idea of fine to coarse category mappings is proposed, whose information is combined with the fusion of feature descriptors resulting in a single feature representation. This additional information enhances performance by exploiting hierarchical relationship among the scene categories. Effectiveness of the proposed approach is validated using different evaluation metrics. Proposed model performs considerably better compared to the given baseline as well as several state-of-the-art methods. Proposed framework ensures appropriate balance between time and accuracy of the model