Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Ship Motion Prediction Algorithm Based on Dual-Pass Long Short-Term Memory Neural Network

When forecasting ship movements, the random errors of the inertial navigation system (INS) seriously affect the accuracy of general prediction methods. In actual measurement, the main causes of the random errors are electrostatic bias and micro-electric disturbance. In response to this problem, a novel type of dual-pass Long Short-Term Memory (LSTM) neural network architecture is developed, on the basis of regular LSTM neural network. In the designed dual-pass LSTM neural network, the random drift and the noise residual of the INS are regarded as a autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH) model. Through dual-pass layers, the prediction of drift and the correction of residual errors are realized respectively in the same time. The simulation of ship heave motion was carried out on the ship motion simulation platform, and the real-time datas which are measured by the INS are inputted to the trained dual-pass LSTM netural network. The experiment proved that, when training the same source datas offline, the average Root Mean Squared Error (RMSE) percentage of conventional LSTM network was 3.94%, but when training different source datas or training online, the prediction accuracy obvious decline. In contrast, the average RMSE percentage of the dual-pass LSTM neural network was 1.05% when training offline and 1.12% when training online. Compared with conventional LSTM networks, the dual-pass LSTM network is more targeted and has better adaptability in the field of ship-motion prediction, and this network restores the motion prediction to the actual trajectory of a ship more accurately.
All-Bit-Line Read Scheme With Locking Bit-Line and Amplifying Sense Node in NAND Flash

A NAND flash memory senses a cell current in the range of tens of nA, which is smaller than other nonvolatile memories in the read operation. Because of the small cell current, it is difficult to improve the latency and accuracy of the read operation. The improvement of read latency has been a major challenge for the all-bit-line (ABL) sensing architecture because the BL coupling capacitance caused by the off cell limits the BL pre-charge time. In this paper, a high-speed BL pre-charge scheme with an off-cell-like BL locking (OCBLL) is proposed to reduce the BL coupling capacitance between the off-cell BL and the adjacent BL. The deviation of the latch trip voltage caused by the variations in process, voltage, and temperature (PVT) presents another challenge because the deviation increases the read error owing to the small cell current. A sense-out-node amplification (SOA) scheme is proposed to amplify the voltage of the sense-out-node according to the cell current. The SOA scheme generates a large voltage difference despite the small cell current difference. In the simulation with HSPICE models under the same BL-to-BL coupling ratio, it is found that the proposed schemes improve the speed of the BL pre-charge operation by 75% and reduce the read error by 57%.
Static Current Unbalance of Paralleled SiC MOSFET Modules in the Final Layout

Silicon Carbide (SiC) MOSFETs enable enhanced performance of power converters in several applications. Parallel connection of SiC MOSFETs become mandatory for medium power applications due to the current rate of existing modules. A balanced current sharing between paralleled MOSFETs is desired to maximize the power capability of each device, maximizing the power capability of the whole system. This work studies the static current unbalance of two paralleled 1200V-400A SiC MOSFET modules with individual gate driver. Experimental measurements are done focused on parasitic inductance caused by electromechanical layout.
Strain-Induced Electronic Structure and Magnetocrystalline Anisotropy Energy in MnFe2O4 From First-Principles Calculations

The normal spinel MnFe2O4 is investigated employing density functional theory calculations using the generalized gradient approximation approach. Here, we address the effect of volume strain on the electronic and magnetic properties of MnFe2O4. We give special emphasis on the strain-induced effect in the magnetocrystalline anisotropy energy (MAE). We observe that compressive strain reduces the magnitude of MAE and leading toward uniaxial symmetry, whereas extensive strain favors planar anisotropy. The site-decomposed orbital anisotropy indicates that B-site atoms largely contribute to the MAE than the A-site atoms. Combined with its half-metallicity and tunable MAE, our results imply a wide range of scientific and technological applications.
Mode Analysis In Planar Dielectric-Conductor Waveguides Using Genetic Algorithms

Determination of the critical wavenumbers, that appear when solving the Helmholtz partial differential equation for electric and magnetic field components in the case of dielectric waveguides, is treated in this paper as an optimization problem that has multiple solutions. The multimodal optimization problem (MMOP) is solved using genetic algorithms hybridized with a gradient based method. A study of the efficiency of power transmission for the different modes (solutions) is carried out using the analytical solutions for field components, with conclusions regarding the waveguide design for best performance.
A Fault Diagnosis Model of Pumping Unit Based on BP Neural Network

At present, oil recovery is generally carried out by pumping unit in oilfield development. Monitoring working status of pumping unit in real time is vital to avoid inefficient production, prevent and control hidden dangers. The condition diagnosis is one of the important contents which often rely on analyzing and classifying the dynamometer card. The traditional method of dynamometer card identification is insufficient to process the huge dynamometer card data in real time. Moreover, the experience and knowledge of the onsite engineers will affect the accuracy of identification. Therefore, in this paper, Freeman chain code and differential code are adopted to extract the characteristics of the dynamometer card data of the pumping unit. Then BP neural network based diagnosis model is proposed to realize the automatic identification of the fault type of the pumping unit according to the dynamometer card. The experimental result shows that the proposed model achieves an accuracy of 94.79%, thus can be used to assist decision making in actual production.
State Observation and Parameter Identification for Autonomous Heavy Haul Train

Heavy haul train is large inertial and non-linear systems. Many real-time disturbances have a significant impact on autonomous driving control. In order to improve the effect of autonomous control, a new state observation and parameter identification method is proposed. The longitudinal multi-mass dynamics model is established for describing the train performance. The acceleration is calculated by Kalman filter of sampled speed. Resistance force and air braking response are identified by train dynamic model. The state observation method can significantly improve autonomous driving control effects. This method is used in control of heavy train autonomous driving.
B-SDN: A Novel Blockchain-based Software Defined Network Architecture

Software Defined Network (SDN) is an essential technology in the next-generation network, while the original SDN architecture with a single controller has a single point of failure and scalability issues. Nowadays, several multi-controller solutions are proposed to address the above issues. However, multiple controllers also bring malicious administrator threats and state consistency issues. Therefore, in this paper, we design a novel blockchain-based SDN architecture, named B-SDN, utilizing the characteristics of non-tamper and non-repudiation. We also re-construct the control layer of the SDN network with the combination of horizontal and vertical architecture. Finally, extensive experiments are conducted to evaluate the throughput, latency, memory usage, CPU usage, network traffic of the proposed architecture.
Application of Convolutional Neural Network in Intrusion Detection

Advanced persistent threat (APT) attack is an advanced network intrusion technology. Because of its strong concealment, pertinence, persistence and long-term intrusion mechanism regardless of cost, the security of all kinds of core networks is greatly threatened. This paper proposes an intrusion detection model based on hybrid convolutional neural network. Compared with the traditional machine learning model, the hybrid deep learning network structure can mine more complex structure features of the whole network traffic matrix, and extract and encapsulate the unknown malicious behavior features. Firstly, the correlation between different feature spaces in network traffic matrix is extracted by Convolutional Neural Network (CNN). Then, Recurrent Neural Network (RNN) is used to find out the time dependence of intrusion traffic data, fully mine the temporal and spatial characteristics of the whole network traffic matrix, and improve the accuracy of intrusion detection model.
Lightweight Cryptography Algorithms for Resource-Constrained IoT Devices: A Review, Comparison and Research Opportunities

IoT is becoming more common and popular due to its wide range of applications in various domains. They collect data from the real environment and transfer it over the networks. There are many challenges while deploying IoT in a real-world, varying from tiny sensors to servers. Security is considered as the number one challenge in IoT deployments, as most of the IoT devices are physically accessible in the real world and many of them are limited in resources (such as energy, memory, processing power and even physical space). In this paper, we are focusing on these resource-constrained IoT devices (such as RFID tags, sensors, smart cards, etc.) as securing them in such circumstances is a challenging task. The communication from such devices can be secured by a mean of lightweight cryptography, a lighter version of cryptography. More than fifty lightweight cryptography (plain encryption) algorithms are available in the market with a focus on a specific application(s), and another 57 algorithms have been submitted by the researchers to the NIST competition recently. To provide a holistic view of the area, in this paper, we have compared the existing algorithms in terms of implementation cost, hardware and software performances and attack resistance properties. Also, we have discussed the demand and a direction for new research in the area of lightweight cryptography to optimize balance amongst cost, performance and security.
Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.
Interoperability Rating of Wireless Charging Equipment using a Decoupled Impedance Interface

During the design process for wireless charging equipment, it is important to consider interoperability with other designs. This paper presents a mathematical method that uses impedance zones and parameter estimation to visualize the interoperability rating of wireless charging equipment. By introducing a decoupled impedance interface, it works without a reference system and, therefore, only requires knowledge of the device being tested. We describe the influence of electronic circuits and magnetic parameters on impedance zone boundaries. Typical values are given for the crucial parameters. The studied method is compared to electric circuit simulation and measurement results.
Research and application of predictive control based on deep learning modeling

The industrial process is usually a lag inertial system. Predictive control is an effective control algorithm for this kind of system, but a more accurate object model is needed. In this paper, DMC predictive control algorithm is used, which does not need the specific form of the model, only needs the step excitation response data of the model. In this paper, the deep learning algorithm is applied to the modeling of industrial process system. After obtaining the more accurate step excitation response data, the predictive control can be carried out, and the ideal control quality can be obtained. First, the input and output data of the closed-loop system are obtained by adding pseudo-random sequence of appropriate period and amplitude into the control instruction of the closed-loop system. The first-order inertia and delay object are used to fit the characteristics of the object, and the first-order inertia time constant T is obtained by using genetic optimization algorithm. Secondly, a third-order inertial link and DNN deep learning network are embedded in the discrete structure of the third-order inertial model to build the intelligent model structure; In order to ensure that the third-order inertial link is close to the inertia time of the object, the inertia time constant of each link is set to t / 3, the input and output data are sent to the intelligent model for training, and the dnn1 model of the object can be obtained; After adding delay $\tau$ to dnn1 model, the genetic algorithm is used to fit the characteristics of the object, and the delay time $\tau$ is obtained; According to the input and output data, the DNN model with delay $\tau$ is trained for the second time to obtain a more accurate identification model dnn2. Thirdly, step excitation is applied to dnn2 model to obtain excitation response data, which is put into predictive controller to obtain excellent control quality. Finally, the first-order object model identified by the least square method is put into the predictive controller, and the control effect is compared with that of this paper. This method has great practical significance for the design and application of predictive control based on deep learning modeling.
Information Retrieval of Text Analogs Based on the CLAD Method

This paper describes the cross-language plagiarism detection method CLAD (Cross-Language Analog Detector) between the test document and indexed documents. The main difference of this method from existing versions is the detection of plagiarism among multiple languages not only two languages and checking all synonym versions of every term which using during compare documents. While translating terms, it uses the dictionary-based machine-translation method. CLAD's working process consists of document indexing and detection process phases. In this paper, we have described both of these phases.
Test Suite Reduction via Evolutionary Clustering

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% −9.44%) and reduction ratio (4.10% −10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% −0.132%) and code coverage rate (3.34% −6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.
Energy-saving Hybrid Network Integrating WSNs and Crowd Sensing

The main contribution of the whole work proposes a hybrid network method integrating WSNs and crowd sensing, establishes a hybrid model and designs the hybrid mechanism. To solve the high cost, low coverage and inflexible expansion of traditional wireless sensor network, the crowd sensing technology is introduced in WSNs. The maximum network life cycle and minimizing energy utilization is derived on the basis of truncated power-law working principle of hybrid mechanism. Simulation results show that hybrid network can effectively save energy and prolong network lifetime.
Multi-Objective Particle Swarm optimization Applied on the Optimal Reactive Power Dispatch in Electrical Distribution Systems

The main purpose of the optimal reactive power dispatch (ORPD) is to reduce the active power losses by the right coordination of on-load tap-changing transformers (OLTC) and the reactive power resources, that may include capacitor banks and distributed generators. Additional to power losses, the voltage deviation minimization represents another aspect of interest in solving the optimal reactive power flow problem. Hence, the ORPD problem can be approached as a complex multi-objective optimization problem. Incorporating nonlinear functions, both continuous and discrete variables and numerous constraints, this problem requires highly performant computation methods for an efficient calculation of the optimal solution. In this regard, a multi-objective particle swarm optimization (MOPSO) model is applied in this paper, in order to determine the ideal settings of distributed generators, transformers and capacitor banks for the proper operation of a distribution electrical system, in terms of active power losses and voltage profiles.
Design and Evaluation of an FBG Sensor-Based Glove to Simultaneously Monitor Flexure of Ten Finger Joints

This paper demonstrates a fiber Bragg grating sensor-based instrumented glove that simultaneously measures the range of motion of the ten finger joints (five metacarpophalangeal, four proximal interphalangeal and one interphalangeal joint) with very high angular resolution (0.1°). The accuracy and repeatability of all the ten sensors of the glove are compared with a pre-calibrated inertial measurement unit (IMU) sensor. The glove outperforms many earlier reported sensing gloves with a mean error of 0.80°. The standard deviation (1.01°) and range (2.60°) obtained in the reliability test performed on five healthy subjects are also smaller compared to many other gloves. The feasibility of using the glove in real-world applications has been shown by demonstrating a virtual reality (VR) platform that uses a Raspberry Pi-based module to interface the glove with custom VR games running on an Android tablet or a VR headset. VR-based gaming platforms have become popular in the rehabilitation of the upper limb in stroke patients in recent years. The high accuracy and reliability of this glove will enable accurate tracking of the movement quality and recovery progress of stroke patients during the virtual rehabilitation therapy.
Optimal Asynchronous FHMA System with Generalized Orthogonal Hopping Patterns

No-hit zone frequency-hopping sequence set (NHZ-FHS) is a kind of generalized orthogonal hopping pattern. To utilize the orthogonality of NHZ-FHS, the quasi-synchronous FH multi-access (FHMA) system demands the strict access delay, that is, the access delay(τ) must be constrained in no-hit zone (Znh); otherwise, the high hit-probability occurs and the performance of FHMA system degrades seriously. In this paper, we introduce the conception of traditional NHZ- FHS and extend it to the generalized NHZ-FHS. The lower bounds on Hamming correlation of NHZ- FHS are presented, which are the criterions for designing the optimal NHZ- FHS. Applying these FHS sets into the classic MFSK/FHMA systems, the performance comparisons are studied by signal analysis. The simulation results show that the generalized NHZ- FHS not only can offer the orthogonal hopped-frequency and optimal BER performance for FHMA systems with the access delay τ restricted within Znh(i.e., quasi-synchronous case), but also can provide lower hit-probability and better BER for asynchronous FHMA system, compared with traditional NHZ- FHS.
A Runtime and Non-Intrusive Approach to Optimize EDP by Tuning Threads and CPU Frequency for OpenMP Applications

Efficiently exploiting thread-level parallelism has been challenging. Many parallel applications are not sufficiently balanced or CPU-bound to take advantage of the increasing number of cores and the highest possible operating frequency. Moreover, many variables may change according to the system (input set, microarchitecture, and number of cores) or during execution, influencing each parallel region in different ways. Therefore, the task of rightly choosing the ideal configuration (number of threads and DVFS) for each parallel region to deliver the best Energy-Delay Product (EDP) is not straightforward. While the significant number of variables prevents the use of exhaustive search methods, the changing nature of the problem precludes offline strategies. Few solutions are online and synergistically consider thread throttling and DVFS. However, they lack transparency (demand changes in the original code) and/or adaptability (do not automatically adjust to applications at run-time). Our proposed Hoder covers all the characteristics above, optimizing at run-time any dynamically linked OpenMP application, without requiring any code transformation or recompilation. We show Hoder’s efficiency by comparing it to two exhaustive offline and two online search approaches, three state-of-the-art techniques, and regular OpenMP execution, considering different setups (Intel 44-, 16- and 12-core; AMD 8- and 12-core).
Oxygen uptake rate inhibition test: A modified method for priority pollutants

Abstract Improvement of a method based on OECD 209 "Respiration Inhibition Test”; was studied. The method involves measuring the oxygen uptake rates of activated sludge microorganisms exposed to different concentrations of inhibitors. Six organic and three inorganic test compounds listed by EPA as priority pollutants were selected and their IC50 values were determined. More reliable results could be obtained, in terms of inhibition types, by prolonging the exposure time. Instant effect measurements of inhibitors gave misleading data for determining and evaluating the inhibitory behaviours.