Search

• Robust functional form contains true parameters far more often than popular models. • Matches/outperforms widely used regression and Neural Network models. • Finds appropriate balance between Model Fit, Inference and Prediction (MIPs). • Introduces new large-sample DGP test; can use to improve A.I. models. • For MIS field finds Popularity Parameter to be important for predicting citations. This article introduces a versatile functional form for Generalized Linear Models (GLMs) through a simple, yet effective, transformation of the current framework. The models are applied through a new hierarchical bayesian estimation procedure for logistic regression to highly-cited papers in the Management Information Systems (MIS) field. The results are uniformly better, in regards to model fit and inference for in-sample and out-of-sample data, for simulation studies and real-world data applications, requiring very little time to convergence to true population parameters. In simulation studies, I show that the method contains the true parameters nearly three times as often as widely used existing GLMs, and does so while having confidence intervals that are 54.50% smaller, while requiring around two-thirds the number of MCMC iterations as existing bayesian methods. In Scientometric applications the methodology is shown to be highly robust with predictive/classification accuracy, either equaling or exceeding existing methods for identifying highly-cited articles including Artificial Neural Networks (ANN). Thus, the method is shown to be robust to the amount of asymmetry (or symmetry) of the probability of success (or failure) and robust to unbalanced samples and varying Data Generating Processes. Further, the methodology is equivalent to current methods if the data support them and is therefore complementary to existing methods, without loss of interpretability of model parameters. For the MIS field it finds that Popularity Parameter (PP) of an article Keywords can predict whether a paper will be highly-cited (top 25% of highly-cited articles) between two to three years after publication and beyond. Furthermore, given the small number of iterations needed for convergence, the methodology can also be used as a baseline method in Big Data (BD) settings for both Artificial Intelligence (AI) and Machine Learning (ML) contexts as well. [ABSTRACT FROM AUTHOR]

Underwater spectral imaging combined with neural networks provides a practical means for intelligent detection and maintenance of offshore engineering. As an underwater detection technology, the data obtained from underwater hyperspectral imaging can be used for both qualitative analysis and quantitative detection of underwater targets, which is an efficient means of underwater target detection in offshore engineering. In this paper, An underwater hyperspectral imaging system based on PGP spectral structure line scanning imaging has been designed and developed for shallow water detection. It has a spectral range of 400–1000 nm, over 200 channels, a spectral resolution of 2.5 nm, a field of view angle of 35.2°, and a focal length of 25 mm. According to the design results, the system was assembled, calibrated and tested. Aiming at solving the shortcomings of the current underwater hyperspectral system, such as the inability of real-time observation of the scanned image, large data volume and complicated processing, the upper computer software was designed and developed to realize the functions of controlling and adjusting the parameter, real-time displaying the scanned image, reconstructing the image and outputting the data in ENVI standard format to the underwater hyperspectral system. The prototype of the system was used to conduct coral detection experiments in Weizhou Island, and the neural network model was used to classify the experimental data, and the overall classification accuracy was above 98%. The experimental results show that the whole system has good imaging quality and good underwater detection capability, and the developed host computer software greatly reduces the workload of raw data processing, improves the experimental detection efficiency, and provides a feasible technical solution for underwater intelligent detection and analysis. • A line-scanning underwater hyperspectral imaging system has been developed based on the PGP spectroscopic structure. • Development of software for the upper computer, which significantly improves the efficiency of underwater detection. • Combining neural network models to achieve accurate detection and classification of underwater targets. [ABSTRACT FROM AUTHOR]

The rising overheads of data-movement and limitations of general-purpose processing architectures have led to a huge surge in the interest in "processing-in-memory" (PIM) approach and "neural networks" (NN) architectures. Spintronic memories facilitate efficient implementation of PIM approach and NN accelerators, and offer several advantages over conventional memories. In this paper, we present a survey of spintronic-architectures for PIM and NNs. We organize the works based on main attributes to underscore their similarities and differences. This paper will be useful for researchers in the area of artificial intelligence, hardware architecture, chip design and memory system. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the problem of designing delayed state-feedback controller for neural networks with leakage delay. By constructing an appropriate Lyapunov–Krasovskii functional including double integral terms having two different exponential decay rates and utilizing linear matrix inequality (LMI) technique with the help of slack variables, some sufficient conditions for globally exponential stabilization results are obtained by designing of delayed state-feedback controller. The novelty of this paper includes: (i) although many papers dealt with dynamics of neural networks with leakage delay, there is little work on design of feedback controller. Even there is almost no result that deals with the delay-feedback controller for such delay systems, which is the main motivation of this paper; (ii) the derived delay-dependent stability criteria establish the relationship between leakage delay and time delay in the feedback term, which can be easily checked via the MATLAB LMI toolbox; (iii) we consider the general case that the control term is given in the form of B u , where B is an n × m real matrix. Such case is more difficult to handle than the special case that B is unit matrix. It indicates that the controller can be used in all states or in some states by proper selection of the matrix B . The effectiveness of the proposed method in this paper is illustrated via numerical examples. [ABSTRACT FROM AUTHOR]

• The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. • The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. • In light of this choice, the FDNN classifier is deployed to categorize the medical information. • In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% decrease in latency on 20 edge nodes in cloud-edge systems. • These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. Addressing the limitations of traffic-centric approaches in cooperative cloud-edge networks, this paper introduces an adaptive deployment strategy for FDNN using LOA. However, the oversight of network structure hampers load-balancing efficiency in medical data categorization. To overcome this, a novel DRL-LOA approach integrates Deep Reinforcement Learning (DRL) with LOA, considering both network structure and traffic-related data. This DRL-LOA considers both network structure and traffic-related data for optimization tasks. The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. In light of this choice, the FDNN classifier is deployed to categorize the medical information. In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% % decrease in latency on 20 edge nodes in cloud-edge systems. These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. [ABSTRACT FROM AUTHOR]

• Low-cost measurement platform for particulate matter monitoring developed. • Artificial intelligence field calibration procedure of low-cost sensor proposed. • Combination of affine scaling and optimized neural network surrogate employed. • Correlation coefficient improved to 0.86 for particulate matter of size 1 μm. • Root-mean-squared error of 3 μm/m3 (1 μm particles) and 4.9 μm/m3 (10 μm particles) Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM 1 and PM 2.5 , and 0.76 PM 10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3. [ABSTRACT FROM AUTHOR]

Nonparametric cost estimation in construction projects with the use of artificial networks is presented as suitable mainly for the early estimates. These conceptual estimates are based on the variables – namely cost predictors that characterize the project or a facility. Data gathered on the basis of completed projects are combined together and applied to the current project cost estimation process. The aim of the paper is to discuss the opportunities and challenges of the approach based on the artificial intelligence tools to cost estimation of construction works. The proposed approach is based on the concept of nonparametric cost estimation and application of artificial neural networks. The author's idea and intention is to transfer the mechanisms of nonparametric cost estimating to the level of construction works. Neural networks, due to their general capabilities, seem to be a good tool to aid the proposed approach. The paper contains discussion of the proposed approach and its applicability. [ABSTRACT FROM AUTHOR]

The paper presents the results regarding the identification of the value of the pull-off adhesion between a concrete added layer with a constant thickness and a substrate concrete layer in existing elements. A method of identification, which is based solely on the use of artificial neural networks (ANNs) and two non-destructive acoustic methods: impact-echo and impulse response on the surface of the added layer, was developed. The methodology of identifying the pull-off adhesion between a concrete added layer and a substrate layer in existing elements was developed and presented in the paper and is useful in construction practice. [ABSTRACT FROM AUTHOR]

Currently, there is a need in society to use digital and cybernetic methods of remote diagnostics of a person's mental state. Using a digital or cyberphysical system in the process of remote recognition of the patient's mental state, it is possible to increase the effectiveness of recognizing the actual psycho-emotional state of the patient, both in the consulting mode and in dynamic observation. For remote assessment of the mental state, methods are widely used based on recognition of facial activity, which is reflexively associated with the display of the psycho-emotional state. In this paper, the authors propose a new approach to creating such a methodology. This work was supported by the RFBR grant 18-07-00987. [ABSTRACT FROM AUTHOR]

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set. [ABSTRACT FROM AUTHOR]

Abstract Functional networks (FNs) are extensions of neural networks (NNs). Unlike NNs, FNs considers general functional models instead of sigmoid-like models. Additionally, in FNs, there are no weights associated with the links that connect neurons. In this paper, we review the research progress and applications of FNs models in recent years. First, we introduce FNs architecture, three typical functional models and the learning process, and we explain the differences between NNs and FNs. Second, we discuss recent applications of FNs that have been introduced in many fields, such as time series prediction, differential and functional equations, pattern classification, detection and prediction, approximation computation, complex system modeling, computer-aided design (CAD), and linear and nonlinear regression. Finally, we present some remarks on future research directions for FNs. [ABSTRACT FROM AUTHOR]

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set. [ABSTRACT FROM AUTHOR]

Abstract Using tools from artificial intelligence, mainly artificial neural networks, this paper presents an walking-engine for humanoid robots. This engine uses dynamic neural networks with feedback for gait generation, a modified Zhang neural network for a singularity-robust inverse kinematics solver, and feedforward neural networks for neuro-adaptive control. The Atlas humanoid robot model in simulation is used to test and verify the capabilities of the neuro-dynamic walking engine. Results show that the engine is capable of generating conventional ZMP stable walking gaits and executing them using the Atlas robot in simulation. Highlights • Develop, implement, and test the feasibility of a neuro-dynamic walking-engine. • Presents abstraction of a neural-process based walking engine. • Dynamic-model-free humanoid that is robust and can adapt to dynamic uncertainties. • Develops a novel feedback Dynamic Neural Network for gait generation. • Modified Zhang Neural Network for singularity robust Inverse Kinematics. [ABSTRACT FROM AUTHOR]

This paper is concerned with leaderless synchronization of coupled delayed neural networks. A distributed event-triggered control strategy under the periodic sampling scheme is introduced to reduce control updates. By introducing a weighted average state as a virtual leader, the leaderless synchronization problem can be transformed to the stability problem of the error system, which is defined as the distance between each node and the virtual leader. A leaderless synchronization criterion under the periodic event-triggered scheme in strongly connected networks is first derived based on Finsler’s lemma. The results are then extended to the network containing a directed spanning tree. A corollary with the lower dimension in the form of LMIs is further presented. Two examples are provided to validate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Due to the energy crisis and the awareness of sustainable development, the research on energy-efficient buildings has increasingly attracted attention. To achieve this objective, one important factor is to capture occupancy properties for building control systems, which refers to occupancy modeling in buildings. Due to the complexity of building occupancy, previous works try to simplify the modeling with some specific assumptions which may not always hold. In this paper, we propose a Generative Adversarial Network (GAN) framework for building occupancy modeling without any prior assumptions. The GAN approach contains two key components, i.e. a generative network and a discriminative network, which are designed as two powerful neural networks. Owing to the strong generalization capacity of neural networks and the adversarial mechanism in the GAN approach, it is able to accurately model building occupancy. We perform real experiments to verify the effectiveness of the proposed GAN approach and compare it with two state-of-the-art approaches for building occupancy modeling. To quantify the performance of all the models, we define five variables with two evaluation criteria. Results show that our proposed GAN approach can achieve a superior performance. [ABSTRACT FROM AUTHOR]

We present artificial neural network (ANN) based software for rapidly predicting the recovery effectiveness (REN) of aquifer storage and recovery (ASR) wells in fresh water aquifers. The REN performance criterion equals the amount of the water injected in an ASR well that is recoverable via the same well during a specified extraction period. The software circumvents the need to prepare and run computationally intensive simulations, by invoking ANN models as surrogate simulators. The paper presents the development of the ANN models and a graphical user interface (GUI) to facilitate using the ANN models and to perform sensitivity analysis. ANN inputs include all factors significantly affecting REN in freshwater aquifers. The software can evaluate REN in any ASR system in confined aquifers whose parameters are within the range of parameters used for training the ANNs in this study. [ABSTRACT FROM AUTHOR]

Feature extraction is a key algorithm to solve the dimensionality problem. Most feature extraction algorithms use a batch mode, which requires all data available at the same time to calculate new features. Recently, with more available data and advancements of transmission technology, the need for incremental algorithms has increased. In this paper, we propose a gradient descent DBFE method (GDDBFE) that shows a substantial improvement in processing time. Based on this GDDBFE, we then propose an incremental gradient descent decision boundary feature extraction method (IGDDBFE). The proposed IGDDBFE method consists of two steps: updating the decision boundaries and adding discriminately informative features with newly added samples and then updating the feature vectors by incremental eigenvector updates. Experiments with real-world databases show that the proposed method shows improved performance compared to some existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the anti-synchronization of master–slave neural networks with time delays. By dividing the whole anti-synchronization process into two procedures: the absolute value of error state e i ( t ) flowing from the initial state to 1, then from 1 to 0, i = 1 , … , n , it gets a new viewpoint and a clear illustration on how controller works to the systems. And combining the H o ¨ lder inequality and other techniques, rigorous analysis gives that, each component of error state e ( t ) ( t  ≥ 0) would flow to 1 in finite time, and continue to flow to 0 in fixed time. A numerical example is presented to illustrate the efficiency and effectiveness of our obtained results. [ABSTRACT FROM AUTHOR]

Artificial intelligence, including neural networks, deep learning and machine learning, has made numerous progress and offered new opportunity for academic research and applications in many fields, especially for business activities and firm development. This paper summarizes different applications of artificial intelligence technologies in several domains of business administration. Finance, retail industry, manufacturing industry, and enterprise management are all included. In spite of all the existing challenges, we conclude that the rapid development of artificial intelligence will show its great impact on more fields. [ABSTRACT FROM AUTHOR]

This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper demonstrates how different scientific disciplines can be successfully merged for efficient real life applications. We propose a novel approach to a social science application using artificial intelligence, by suggesting a neural network designto anticipate or predict people’s perceptions regarding the Cyprus conflict and the peace mediation process.Our novel model is based on associating people’s views and thoughts on critical issues related to the conflict together with their anticipation for securing a peace treaty by year 2020. Our database is our own and has been collected through completed surveys by the international relations department students at the Near East University. The obtained digitally-coded values of the entire database as well as the proposed associative neural predicting model can be further used in training and testing the neural model, which certainly waives the way for further research in this rare application area for artificial intelligence. [ABSTRACT FROM AUTHOR]

The fate of many construction projects is determined using preliminary project cost estimates. These estimates play a key role during the conceptual phase of projects; as in many cases they are the primary element used to decide their viability. The lack of information and the high levels of uncertainty during the conceptual phase, make it infeasible to have reliable building information models that could be used to generate quantity takeoffs for preliminary cost estimates (known in the industry as 5D BIM), in line with a Level 2 BIM maturity. This paper presents a way to combine artificial intelligence (case-based reasoning and neural networks), with traditional techniques (regression analysis), to develop accurate estimates of the resources needed in a project (e.g., construction material quantities). These estimates of resources can then be coupled with unit cost information to make preliminary resource-based cost estimates. The clear division between the technical and financial components of such an estimate gives improved decision support to project managers and decision makers. This enhances the tracking and control mechanisms which could be used to check the estimates prepared in subsequent project phases. The combination of case-based reasoning with regression analysis and the use of neural networks has shown an improved performance in the estimation of the amount construction material quantities. The proposed combination was used to estimate the amount of concrete, reinforcement, and structural steel required for the construction of tall-frame structures. The results show lower errors (overall mean absolute percentage error-MAPE) for the combined models (2.55%) when compared to the regression models (12.01%), neural network models (5.84%), and case-based reasoning models (9.30%). [ABSTRACT FROM AUTHOR]

Rockfall risk management has traditionally been built upon scenarios based on statistical analysis of historic frequency-magnitude rockfall patterns which allows for an estimation of the return period of rockfalls within given ranges of magnitude. Τhe availability of rockfall databases is thereby of crucial importance to quantify rockfall activity. In recent years, engineering geologists have been able to leverage light detection and ranging (LiDAR) technology with automated change detection and clustering workflows, for spatially accurate rockfall monitoring and inventorization. This wealth of digital data generates new research prospects for the development of an artificial intelligence (AI) system able to analyze change detection sequences and map spatio-temporal rockfall patterns by precisely capturing the rock slope evolution in near-real-time. However, prerequisite to such an advancement is the need to efficiently automate the process of classifying surface changes corresponding to rockfall. This task typically requires intensive human effort and is prone to multiple sources of human error like subjectivity, expertise, and experience that may differ from person to person involved in a particular project. This paper investigates the potential of integrating sophisticated deep learning architectures into dynamic rockfall database population processes, with the goal of relieving experts from the daunting task of manually classifying rockfall events within large loads of change detection data. Deep neural networks based on the pioneering PointNet and PointNet++ architectures for 3D point cloud learning are developed for this purpose based on a 5-year change detection database consisting of >8000 rock slope clusters of identified change for training, with scanning intervals ranging from 5 days to 6 months. The models are tested on the 536 clusters from the two last data acquisitions to simulate the real monitoring situation and subsequently on the most frequent of the campaigns to increase the probability of working with single-event clusters. The best-performing model achieves an accuracy of about 89% and 84% on the last and shortest campaign, respectively. The optimized deep learning models are further evaluated on a geologically different rockfall database achieving almost 93% accuracy in a location where discrete geomorphologic features such as steep rock outcrops and erosion channels are present. The study shows that although it is challenging to achieve generalization in rockfall detection, site-specific training of the proposed deep learning architecture can lead to high-level performance and support further advancements in rockfall risk management. • LiDAR monitoring provides detailed 3D point cloud models of rock slope deformations. • High-level feature learning from point clouds is achieved with modern deep learning. • Rockfall detection can be approached as a binary point cloud classification problem. • Global shape feature learning is suggested compared to local texture features. • The prediction confidence increases with the cluster volume. [ABSTRACT FROM AUTHOR]

Image/Video Quality Assessment (IQA/VQA) plays a significant role in image and video processing, as it can directly predict the impact of distortions on the video in the quality of experience (QoE) of the user. For this propose, in this paper, it is presented a new method for objective video quality assessment using an artificial neural network to predict the subjective evaluation of the video as if it were observed by a human user. The network was trained using degradation indicators extracted from the VQEG Phase I video database, which describe the level of distortion suffered by the original video under spatial and temporal scopes. The proposed method obtained an excellent correlation with the subjective scores over this same database. [ABSTRACT FROM AUTHOR]

A three-phase intelligent technique has been constructed to improve the data-hiding algorithm in colour images with imperceptibility. The first phase of the learning system (LS) has been applied in advance, whereas the other phases have been applied after the hiding process. The first phase has been constructed to estimate the number of bits to be hidden at each pixel (NBH); this phase is based on adaptive neural networks with an adaptive genetic algorithm using upwind adaptive relaxation (LS ANN _ AGAUpAR1 ). The LS of the second phase (LS ANN _ AGAUpAR2 ) has been introduced as a detector to check the performance of the proposed steganographic algorithm by creating a rich images model from available cover and stego images. The LS of the last phase (LS CANN _ AGAUpAR3 ) has been implemented through three steps, and it is based on a concurrent approach to improve the stego image and defend against attacks. The adaptive image filtering and adaptive image segmentation algorithms have been introduced to randomly hide a compressed and encrypted secret message into a cover image. The NBH for each pixel has been estimated cautiously using 32 principle situations (PS) with their 6 branch situations (BS). These situations have been worked through seven layers of security to augment protection from attacks. In this paper, hiding algorithms have been produced to fight three types of attacks: visual, structural, and statistical attacks. The simulation results have been discussed and compared with new literature using data hiding algorithms for colour images. The results of the proposed algorithm can efficiently embed a large quantity of data, up to 12 bpp (bits per pixel), with better image quality. [ABSTRACT FROM AUTHOR]

In this paper we present the modeling of a neuro-fuzzy system to be applied in the mix of paint from color model cyan, magenta and yellow (CMY). We aim to research an efficient method to get a specific hue color that is located within color wheel. The modeling is focused in to generate a wide gamma of colors to paint crafts like Panama Hats only with mentioned colors. Variables of the system are given by the frame and shape of selection of color. We use a fuzzy controller to generate the magnitude of the CMY percentages and a neural network to interpret the components to dosage the assigned color. Finally proceed data are used to develop an embedded controller under open source, the resulting color from the system is verified using different samples to determine the accuracy with selected color. [ABSTRACT FROM AUTHOR]

Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural network to treat natural language. Most of the conventional neural networks can only process sentences consisted of a few words, and their applications are very simple such as metaphor understanding. The proposed network can process many complicated sentences and can be used as an associative memory and a question-answering system for factoid questions. The proposed network is composed of three layers and one network: Sentence Layer, Knowledge Layer, Deep Case Layer and Dictionary Network. The input sentences are divided into knowledge units and stored in the Knowledge Layer. The Deep Case Layer plays an important role in processing the knowledge units properly. The Dictionary Network also plays an important role as a knowledge base. We have carried out several experiments and they have shown that the proposed neural network has superior performances as an associative memory and a question-answering system. Especially as a question-answering system, the performance is very close to the elaborated system based on artificial intelligence. [ABSTRACT FROM AUTHOR]

Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional–integral–derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity. [ABSTRACT FROM AUTHOR]

Differential pressure flowmeters are very often used in many industries. Therefore, the improvement of this method of flow measurement is an important task of flow measurement and instrumentation. One of the important characteristics of differential pressure flowmeters is the discharge coefficient of the flow transducers. A large number of studies and publications were devoted to modeling this coefficient. Therefore, in the framework of this research, this coefficient is simulated using artificial neural networks. The neural representation of this characteristic is made in the form of a multilayer perceptron. In this paper, we replace the traditional equation for the discharge coefficient with an artificial neural network. The advantages and disadvantages of such application of neural networks as discharge coefficients are discussed. The analysis of the results of gas flow measurement, where the neural network is used instead of the traditional equation, is presented. The estimation of flow rate measurement errors with such an approach is made; the error of calculation of the discharge coefficient is estimated. • Simulation of differential pressure flowmeters using neural networks. • Neural representation of the discharge coefficient of orifices plates. • Test gas flow measurement system with neural networks. [ABSTRACT FROM AUTHOR]