Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Multiple Model Particle Filtering for Bearings-Only Maneuvering Target Tracking

This paper presents an effective constrained multiple model particle filtering (CMMPF) for bearings-only maneuvering target tracking. In the proposed algorithm, the process of target tracking is factorized into two sub-problems: 1) motion model estimation and model-conditioned state filtering according to the Rao–Blackwellised theorem and 2) the target dynamic system is modeled by multiple switching dynamic models in a jump Markov system framework. To estimate the model set, a modified sequential importance resampling method is used to draw the model particles, which can be restricted into the feasible area coincide with the constrained bound. To the model-conditioned state nonlinear filtering, a truncated prior probability density function is constructed by utilizing the latest observations and auxiliary variables (target spatio–temporal features), which can guarantee the diversity and accuracy of the sampled particles. The tracking performance is compared and analyzed with other conventional filters in two scenarios: 1) uniform and time-invariant sampling scenario and 2) non-uniform and sparse sampling scenario. A conservative Cramer–Rao lower bound is also introduced and compared with the root mean square error performance of the suboptimal filters. Simulation results confirm the superiority of CMMPF algorithm over the other existing ones in comparison with respect to accuracy, efficiency, and robustness for the bearings-only target tracking system, especially for the aperiodic and sparse sampling environment.
Surge-Varying Adaptive LOS Based Path Following Control of Underactuated Marine Vehicles with Adaptive Sideslip Compensation and fuzzy Uncertainty Observation

Subject to complex uncertainties, a novel surge-varying adaptive LOS (SALOS) based path following control scheme is addressed for underactuated marine vehicles (UMVs) in this paper. Main contributions are as follows: (1) The surge velocity and heading angle are guided with path following errors, simultaneously, and the unknown sideslip angle is estimated by an adaptive term online. (2) Complex unknown uncertainties including the internal parameter perturbations, external environmental disturbances and unmodelled dynamics are compositely estimated by the constructed fuzzy uncertainty observer (FUO); (3) Robust surge and heading tracking controllers based on the FUO are developed to ensure that the tracking errors and uncertainty observation errors are uniformly ultimately bounded (UUB). Simulation studies are conducted to demonstrate the effectiveness and remarkable performance of the proposed SALOS scheme.
The Patent Portfolios Value Evaluation Based on the TOPSIS-Entropy Method

How to evaluate the value of patent portfolios in a competitive market has attracted increasing attention. At present, most existing methods have to determine the weightings of multiple indicators based on expert judgements in the process of patent value evaluation. In this paper, we propose a new approach to value firms' patent portfolios by combining Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which can be called the TOPSIS-Entropy method. The weights of multiple indicators can be determined by using Entropy, while the ranking of patent portfolios value is calculated by TOPSIS. A case of applying the TOPSIS-Entropy method is given in the paper. The TOPSIS-Entropy method is proved reasonable and suitable for patent portfolios value evaluation.
Real Time Building and Maintaining Causal Congestion Graph for Intelligent Traffic Management

Traffic congestion is a major problem for commuters and it has several negative impacts on environment and economy alike. Existing congestion detection techniques are mostly centralized in nature. In this paper, we proposed a distributed and localized congestion detection approach which seems more suited to real-time congestion detection across large-scale road network. Our algorithm studies causal relation between congested road intersections, i.e., how the congestion propagates from a point in the road network to all directions and can further predict the possible propagation pattern. We have evaluated our algorithm using GPS traces of more than 2299 taxis in Shanghai collected from Jan. 31 to March 1, 2007. We study the repetitive nature of traffic on weekdays and weekends across 281 physical locations (sites) and 500 road intersection points. The results show that we can predict future congestion patterns by analyzing real time causal congestion relations with accuracy up to 65%.
Towards the Machine Reading of Arabic Calligraphy: A Letters Dataset and Corresponding Corpus of Text

Arabic calligraphy is one of the great art forms of the world. It displays Arabic phrases, commonly taken from the Holy Quran, in beautiful two-dimensional form. The use of two dimensions, and the interweaving of letters and words makes reading a far greater challenge for Artificial Intelligence (AI) than reading standard printed or hand-written Arabic. To approach this challenge, we have constructed a dataset of Arabic calligraphic letters, along with a corresponding corpus of phrases and quotes. The letters dataset contains a total of 3,467 images for 32 various categories of Arabic calligraphic-type letters. The associated text corpus contains 544 unique quoted phrases. These data were collected from various open sources on the web, and include examples from several Arabic calligraphic styles. We have also undertaken both an explorative statistical analysis of this data, and initial machine learning investigations. These analyses suggest that combining knowledge of a limited variety of Arabic calligraphy texts, with a successful machine will be sufficient for the machine reading of forms of Arabic calligraphy.
Demo Abstract: Walkway Discovery from Large Scale Crowdsensing

Existing digital maps mainly focus on motorways and miss many walkways facilitating people's daily mobility especially for pedestrians. Based on in-depth analysis of massive human mobility trajectories collected from the National Science Experiment (NSE) in Singapore, we propose a system, discovering walkways from the large scale crowdsensing mobility data. In this demo, we show the new-found walkways discovered by our system in Singapore through a custom visualization platform.
Deep Reinforcement Learning Combustion Optimization System Using Synchronous Neural Episodic Control

Neural episodic control (NEC) is an emerging deep reinforcement learning algorithm, the dynamic-scale memory buffer of which is updated by appending experience in a steady stream. NEC has greater ability to utilize the information of the memory and outperforms other deep reinforcement learning agents. However, the scale of memory consumption increases dramatically along with the training episodes. It is even worse that, to query the large-scale memory buffer, kd-trees based approximate nearest neighbors algorithm has to be repeatedly performed. These defects of NEC consequently hinder the further application on the industrial control units with low-performance hardware; e.g. the thermal power plant. To overcome this problem, in this paper, a deep reinforcement learning system based on the synchronous neural episodic control (SNEC) algorithm is proposed for combustion optimization tasks on limited memory resources. The proposed system consists of two major components: 1) a predictor on the basis of convolutional neural networks and LSTM module; 2) an SNEC optimizer agent. The main research and innovations are as follows: 1) a deep neural network model is developed to predict system states accurately and serves as a proxy for the optimizer; 2) a fixed-scale memory buffer is designed specifically for the control agents with low-performance hardware; 3) a straightforward mechanism for lookup and update is established to eliminate the computation of kd-trees search algorithm; 4) different from the original one, only one public differentiable neural dictionary (PDND) is required for SNEC agent, and thus the Q-value for all the actions are updated synchronously. The experimental results show that the proposed SNEC algorithm outperforms the standard deep Q-networks on limited memory resources; with the application of this system, the power efficiency turns out to be improved, and original gas emissions are reduced. In addition, the control actions are designed to be simple enough for the practical implementation.
A Circular-Polarization Reconfigurable Meng-Shaped Patch Antenna

In this paper, we introduce a reconfigurable artistic patch antenna with unidirectional circular-polarization (CP) radiation. The novel geometry composed of a Chinese character Meng ( <fig position="float" orientation="portrait"> <graphic position="float" orientation="portrait" xlink:href="chung10-2869410.eps"/> </fig>)- shaped patch and dual L-shaped feeding probes that are connected to a PIN-diode-based switching circuit. The simulated and experimental results show that the proposed antenna achieves the operating bandwidth from 2.4 to 2.5 GHz that covers dynamically the 14 channels of IEEE 802.11/b/g/b/ax. By switching the L-probes, either left-handed CP or right-handed CP can be generated to cover different seven-channel blocks in order to enhance the channel efficiency. A broadside realized gain varying between 5–6 dBic and an efficiency of −0.5dB were recorded. The proposed antenna holds an overall dimension of <inline-formula> <tex-math notation="LaTeX">$0.72\lambda _{0}\times 0.72\lambda _{0}\times 0.12\lambda _{0}$ </tex-math></inline-formula> with an aperture efficiency of 59% at 2.45 GHz.
Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Deep learning systems have recently gained importance as the architecture of choice in artificial intelligence (AI). Handwritten numeral recognition is essential for the development of systems that can accurately recognize digits in different languages which is a challenging task due to variant writing styles. This is still an open area of research for developing an optimized Multilanguage writer independent technique for numerals. In this paper, we propose a deep learning architecture for the recognition of handwritten Multilanguage (mixed numerals belongs to multiple languages) numerals (Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic). The overall accuracy of the combined Multilanguage database was 99.26% with a precision of 99.29% on average. The average accuracy of each individual language was found to be 99.322%. Results indicate that the proposed deep learning architecture produces better results compared to methods suggested in the previous literature.
Re-evaluating the assistant practitioner role in NHS England: Survey findings.

AIM AND BACKGROUND In the absence of data providing an overview on the state of the assistant practitioner (AP) workforce, this study surveys trusts in NHS England with the aim of establishing how the role is viewed, used and managed. METHODS Based on an earlier survey undertaken around a decade ago, an online questionnaire was sent to members of an assistant practitioner network, generating a response from over fifty different trusts, drawn from different regions and health care settings. RESULTS The survey results highlight the increased use of assistant practitioners by trusts and in a more diverse range of clinical settings. This increase has been driven more by the apparent value of the APs in addressing issues of service design and quality, than by attempts to reduce costs through substitution and skill mix dilution. CONCLUSIONS The AP role has retained value to nurse managers in developing and designing services, and indeed in establishing a career pathway for health care assistants. Most striking are future intentions to continue using APs, particularly within the context of the emerging nursing associate (NA) role. This suggests that the AP and NA are likely to be complementary rather than alternative roles. IMPLICATIONS FOR NURSING MANAGERS Nurse managers might note the continuing use and value of the AP role, although as a means of improving design and quality as well as providing career opportunities for health care assistants, rather than as a way of saving labour costs. Clearly, the AP role has a future although there is scope to review its position in relation to the newly emerging nursing associate role.
Cortical auditory evoked potentials in cochlear implant listeners via single electrode stimulation in relation to speech perception

Abstract The objectives of this study were to investigate the effects of place of stimulation on cortical auditory evoked potentials in relation to speech performance in cochlear implant listeners. It was designed that cortical responses were recorded for single-electrode bursts at apical, medial and basal portions of the electrode array with varying inter-stimulus intervals ranging from 300 ms to 5 s. Latency and amplitude of N1 and P2 peaks were analysed in relation to monosyllabic word scores. The study sample was 44 adult cochlear implant users ranging in age from 28 to 86 years. N1, P2 and N1–P2 amplitudes declined significantly from apical to basal electrodes. The most robust and pronounced responses were recorded for slower stimulation rates (5 s). Speech recognition correlated positively with N1 and N1–P2 amplitudes at the medial electrode. P2 latency showed a significant negative correlation with speech performance at the apical electrode. At last, cortical responses varied significantly depending on the stimulation site and rate. We can objectively quantify speech performance with the N1, N1–P2 amplitude and P2 latency in cochlear implant users. Deafness-related neural degeneration persists even after the cochlear implantation and is more distinct at the base than the apex of the cochlea.
Connected Autonomous Vehicle Platoon Control Considering Vehicle Dynamic Information

Considering the effect of vehicle dynamic information, this study proposed a platoon control protocol for connected autonomous vehicles driving on the lane-disciplined roads under the vehicle-to-vehicle (V2V) communication environment. Algebraic graph theory has been used to capture the communication connections between vehicles. In addition, the electronic throttle opening angle containing the vehicle dynamics information is incorporated in the control protocol. The stability and consensus of the proposed control protocol are verified using Routh-Hurwitz stability criterion. Furth, the performance of the control protocol is that vehicle states can reach the consensus with respect to spacing error and velocity under external perturbation. Finally, the effectiveness of the proposed protocol is verified by the numerical experiment.
Why is Siemens establishing its robotics R&D centers in China? A case study on the Siemens industrial robot project

China has been transforming its development strategy from an investment-driven to an innovation-led one. Recent government policies (e.g., "Made in China 2025") have shown the country’s ambition to upgrade its manufacturing industry. Meanwhile, the technological environments and market conditions in China have also been changing rapidly. All these changes in terms of government policies, technological environments, and market conditions bring both opportunities and challenges for multinational enterprises (MNEs) in China. How should MNEs react to such new realities? As one leading MNE with a long history in the Chinese market, Siemens has been adapting its global R&D strategies, establishing its R&D centers of industrial robots in China. This represents an interesting case to academic researchers, industry practitioners, as well as policy makers. To better understand the co-evolution of MNEs and China’s innovation systems, we conducted this case study project on the Siemens innovation about industrial robots. The research shows that, on the one hand, Siemens’ decision of conducting R&D on industrial robots in China is driven by China’s evolving host-country environments. On the other hand, the changing R&D strategies of Siemens in China also have impacts on China’s innovation system, helping domestic manufacturers to make more advanced robots.
A Novel Bacterial Algorithm for Parameter Optimization of Support Vector Machine

Support Vector Machine (SVM) plays an important role in solving the problem of data classification. However, the traditional parameter optimization method of SVM is difficult to implement and has a low precision. In order to overcome this problem, Cauchy mutation and simulated annealing (SA) algorithm are firstly applied to improve the performance of bacterial foraging optimization (BFO) algorithm. And a novel bacterial algorithm SCBFO is firstly proposed for the parameter optimization of SVM. Moreover, the validity and efficiency test of the model is strictly evaluated through learning data set the 4 UCI machine learning datasets. Three existing algorithms are employed as a contrast. The simulation results demonstrate that the proposed SCBFO-SVM method is able to obtain more accurate results.
Intrarenal venous flow in cardiorenal syndrome: a shining light into the darkness

The aim of this case report is to assess the potential role of intrarenal Doppler ultrasonography as a non‐invasive method to evaluate intrarenal venous flow (IRVF) in acute heart failure (AHF) and concomitant renal dysfunction. We report a case of an 81‐year‐old woman with valvular heart disease (previous mitral valve replacement) that presented with acutely decompensated heart failure and concomitant worsening renal function (WRF). In addition to complete physical examination, laboratory analysis, and echocardiography, IRVF was assessed at baseline and 48 h after the administration of diuretic treatment. At admission, physical examination and echocardiography revealed signs of intravascular congestion (jugular venous distension and severely dilated inferior vena cava). In addition, a significant increase in serum creatinine from 1.23 to 1.81 mg/dL was noted without signs of hypoperfusion at clinical evaluation. At baseline, intrarenal Doppler ultrasonography showed a monophasic IRVF pattern indicating a severely elevated interstitial renal pressure. After aggressive decongestion, a dynamic behaviour was found in IRVF changing from monophasic to biphasic pattern in parallel with an improvement in clinical parameters and renal function (serum creatinine changed from 1.81 to 1.44 mg/dL). In this case of a patient with AHF and WRF, IRVF changed after aggressive decongestion in agreement with clinical evolution. According to these findings, this technique could provide valuable information for identifying patients with a ‘congestion kidney failure’ phenotype. Further studies are needed confirming this observation and evaluating the potential role of this technique for guiding decongestive therapy in patients with AHF and WRF.
Modeling and Optimization of Paper-making Wastewater Treatment Based on Reinforcement Learning

Environmental disturbances and system uncertainties are the main obstacles for wastewater treatment process of paper-making process. In this paper, a neural network based on on LSTM which is a neural network that can learn long-term dependencies is constructed to simulate the environment of the above process. A deep reinforcement learning method is then proposed to optimize the wastewater treatment process. With the proposed design, the control scheme could not only obtain a good performance of the control system, but also can enhance the robustness of the closed-loop system. Numerical simulations are given to demonstrate the effectiveness of the proposed method.
Limited Communication Consensus Control in Multi-UUVs Swarm System Under Switching Topologies and Time Delay

Multiple unmanned underwater vehicles (multi-UUVs) in the swarm can effectively carry out a task when they are successfully exchanging information among themselves in the ocean, so the paper mainly investigates the limited communication consensus control problem for the multi-UUVs swarm systems. The paper proposes a hybrid structure of the multi-UUVVs swarm. The multi-UUVs swarm system will be divided into many sub-groups in where it will be composed of one leader and many followings of UUVs, meanwhile, all leaders in the sub-groups will constitute a leaders-group. Two suitable Lyapunov-Krasovskii functional candidates will be given to obtain sufficient consensus conditions and multiple randomly varying leader-following consensus stability criterion for Markovian switching swarm system. Then the swarm system will be proved to be stochastically stable. Finally, a numerical example is shown to demonstrate the effectiveness of the discussed methods.
Optimization of Toll Plaza Based on Progressive Analysis

With the continuous development of modern technology, expressway toll stations take more efficient and innovative way, making the drivers own a better service experience when passing through the toll station. Whether the performance of the toll station is superior depends on whether the construction of the toll plaza is reasonable. The merits and demerits of a toll plaza need to be considered from the aspects of safety, traffic carrying capacity and fee structure. In this passage, we use the hydrodynamic model to replace the discrete and macroscopic vehicle groups with continuous and microscopic water flow. We get the conclusion that the trapezoidal region is most suitable for vehicle merging from the low curvature fusiform orbit. At the same time, we use cellular automaton to simulate the process of vehicle merging, and verify the conclusion that the merge area should be trapezoidal. On this basis, according to the different ways of charging, we propose the construction of irregular multi-level trapezoidal lane area according to different service level. Next, we establish a layer-by-layer progressive analysis model to analyze the relationship between the merge method and security level, road throughput and cost, so we can get the best way to construct the toll plaza under certain expectation and requirement. We have analyzed the construction of the square under the general conditions, and obtained the conclusion that the double side single joint method can meet the requirements of traffic safety and road throughput to the maximum extent. And the size of the toll plaza is proportional to the traffic volume and the speed of the merge.
Effect of sulfadiazine and trimethoprim on activated sludge performance and microbial community dynamics in laboratory‐scale membrane bioreactors and sequencing batch reactors at 8°C

The effect of antibiotics sulfadiazine and trimethoprim on activated sludge operated at 8°C was investigated. Performance and microbial communities of sequencing batch reactors (SBRs) and Membrane Bioreactors (MBRs) were compared before and after the exposure of antibiotics to the synthetic wastewater. The results revealed irreversible negative effect of these antibiotics in environmentally relevant concentrations on nitrifying microbial community of SBR activated sludge. In opposite, MBR sludge demonstrated fast adaptation and more stable performance during the antibiotics exposure. Dynamics of microbial community was greatly affected by presence of antibiotics. Bacteria from classes Betaproteobacteria and Bacteroidetes demonstrated the potential to develop antibiotic resistance in both wastewater treatment systems while Actinobacteria disappeared from all of the reactors after 60 days of antibiotics exposure. Altogether, results showed that operational parameters such as sludge retention time (SRT) and reactor configuration had great effect on microbial community composition of activated sludge and its vulnerability to antibiotics. Operation at long SRT allowed archaea, including ammonium oxidizing species (AOA) such as Nitrososphaera viennensis to grow in MBRs. AOA could have an important role in stable nitrification performance of MBR‐activated sludge as a result of tolerance of archaea to antibiotics. © 2018 American Institute of Chemical Engineers Biotechnol. Prog. , 35: e2708, 2019