Special Issue "Intelligence Systems and Sensors"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editor

Prof. Dr. Youngchul Bae
E-Mail Website
Guest Editor
Department of Electrical and Semiconductor Engineering, Chonnam National University, Daehak-ro 50 Yeosu, Jeonnam 59626, Korea
Interests: intelligent system; fuzzy and neural network; laser range finder; laser interferometer; localization; IoT sensors; chaos synchronization and control; robot control
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to cordially invite you to contribute a paper to Special Issue of the open access journal Applied Sciences, entitled “Intelligent systems and sensors”, which aims to present recent developments of fuzzy, Neural network and artificial Intelligence in the fields of real life including robot, social system, industries  and so on.

This Special Issue contains fuzzy, neural networks, neuro-fuzzy systems, intelligent systems and sensors, based on artificial intelligent. I invite you to submit your research on these topics, in the form of original research papers and articles.

Prof. Youngchul Bae
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Complex Systems
  • Computational Intelligence 
  • Evolutionary Computing 
  • Fault Detection and Diagnosis 
  • Fuzzy Control 
  • Fuzzy Sets and Logic 
  • Fuzzy Systems 
  • Granular Computing 
  • Intelligent Communications 
  • Intelligent Electronics 
  • Intelligent Electrical Systems 
  • Information Fusion 
  • Intelligent Control 
  • Intelligence sensors 
  • Smart sensors 
  • Imaging sensors
  • Sensor application 
  • Others
  • Intelligent Manufacturing Systems
  • Intelligent Medical Systems
  • Intelligent Systems
  • Intelligent Transportation Systems
  • Machine Learning 
  • Mathematical Models 
  • Neural Networks
  • Neuro-Fuzzy Systems 
  • Robotics 
  • Social Systems
  • Web Intelligence and Interaction

Published Papers (16 papers)

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Open AccessArticle
Double-Step U-Net: A Deep Learning-Based Approach for the Estimation of Wildfire Damage Severity through Sentinel-2 Satellite Data
Appl. Sci. 2020, 10(12), 4332; https://doi.org/10.3390/app10124332 - 24 Jun 2020
Cited by 2 | Viewed by 793
Abstract
Wildfire damage severity census is a crucial activity for estimating monetary losses and for planning a prompt restoration of the affected areas. It consists in assigning, after a wildfire, a numerical damage/severity level, between 0 and 4, to each sub-area of the hit [...] Read more.
Wildfire damage severity census is a crucial activity for estimating monetary losses and for planning a prompt restoration of the affected areas. It consists in assigning, after a wildfire, a numerical damage/severity level, between 0 and 4, to each sub-area of the hit area. While burned area identification has been automatized by means of machine learning algorithms, the wildfire damage severity census operation is usually still performed manually and requires a significant effort of domain experts through the analysis of imagery and, sometimes, on-site missions. In this paper, we propose a novel supervised learning approach for the automatic estimation of the damage/severity level of the hit areas after the wildfire extinction. Specifically, the proposed approach, leveraging on the combination of a classification algorithm and a regression one, predicts the damage/severity level of the sub-areas of the area under analysis by processing a single post-fire satellite acquisition. Our approach has been validated in five different European countries and on 21 wildfires. It has proved to be robust for the application in several geographical contexts presenting similar geological aspects. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection
Appl. Sci. 2020, 10(11), 3706; https://doi.org/10.3390/app10113706 - 27 May 2020
Cited by 4 | Viewed by 709
Abstract
Nowadays, smartphones are an essential part of people’s lives and a sign of a contemporary world. Even that smartphones bring numerous facilities, but they form a wide gate into personal and financial information. In recent years, a substantial increasing rate of malicious efforts [...] Read more.
Nowadays, smartphones are an essential part of people’s lives and a sign of a contemporary world. Even that smartphones bring numerous facilities, but they form a wide gate into personal and financial information. In recent years, a substantial increasing rate of malicious efforts to attack smartphone vulnerabilities has been noticed. A serious common threat is the ransomware attack, which locks the system or users’ data and demands a ransom for the purpose of decrypting or unlocking them. In this article, a framework based on metaheuristic and machine learning is proposed for the detection of Android ransomware. Raw sequences of the applications API calls and permissions were extracted to capture the ransomware pattern of behaviors and build the detection framework. Then, a hybrid of the Salp Swarm Algorithm (SSA) and Kernel Extreme Learning Machine (KELM) is modeled, where the SSA is used to search for the best subset of features and optimize the KELM hyperparameters. Meanwhile, the KELM algorithm is utilized for the identification and classification of the apps into benign or ransomware. The performance of the proposed (SSA-KELM) exhibits noteworthy advantages based on several evaluation measures, including accuracy, recall, true negative rate, precision, g-mean, and area under the curve of a value of 98%, and a ratio of 2% of false positive rate. In addition, it has a competitive convergence ability. Hence, the proposed SSA-KELM algorithm represents a promising approach for efficient ransomware detection. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
A Novel Approach against Sun Glare to Enhance Driver Safety
Appl. Sci. 2020, 10(9), 3032; https://doi.org/10.3390/app10093032 - 26 Apr 2020
Viewed by 723
Abstract
The automotive industry is developing continuously, trying to improve, among others, the safety of drivers, passengers, and pedestrians. Using modern technology, the dangers caused by weather hazards like rain, snow, fog, or glare were identified and reduced. This paper presents an anti-glare solution [...] Read more.
The automotive industry is developing continuously, trying to improve, among others, the safety of drivers, passengers, and pedestrians. Using modern technology, the dangers caused by weather hazards like rain, snow, fog, or glare were identified and reduced. This paper presents an anti-glare solution using existing technologies that can be found already in a high-end car like the driver’s eyes tracking systems, light intensity sensors, or head-up displays. In addition to the existing elements, a sun tracking sensor is required to detect the point where the sun light has the maximum intensity on the windshield surface. Knowing the driver’s position and the point on the windshield where the sunlight has a high intensity, a dark spot can be created on the windshield in order to reduce the discomfort created by glare. Depending on the intensity of the light and taking into consideration the traffic safety laws, the spot’s transparency can vary between certain limits. Therefore, the dangers caused by glare will be diminished and the risks of not observing pedestrians, other traffic participants, traffic signs, or sudden curves will be considerably lower. Another advantage of using a digital sunshade based on smart glass or in-glass transparent displays, instead of a regular sunshade, is that the whole windshield can be protected against glare not just the top of it. The results were verified and highlighted using computer simulations done via a MATLAB environment. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Pedestrian Detection Based on Two-Stream UDN
Appl. Sci. 2020, 10(5), 1866; https://doi.org/10.3390/app10051866 - 09 Mar 2020
Cited by 2 | Viewed by 713
Abstract
Pedestrian detection is the core of the driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing [...] Read more.
Pedestrian detection is the core of the driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down, or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffering from weather, lighting, clothing, large pose variations, and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition, and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources, which make it difficult to put into practical applications. In real scenarios of autonomous vehicles, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice, since it performs well while consuming less computation resources. Based on UDN, this paper proposes a new deep network model named two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees/telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features, and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including the HOG (Histogram of Oriented Gradients) feature, Sobel feature, color feature, and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection, since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps. First, the two sub-networks are trained until converge; then, we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Swish is adopted as the activation function to obtain a faster training speed, and positive samples are mirrored and rotated with small angles to make the positive and negative samples more balanced. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks
Appl. Sci. 2020, 10(3), 930; https://doi.org/10.3390/app10030930 - 31 Jan 2020
Cited by 13 | Viewed by 697
Abstract
The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the [...] Read more.
The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
Appl. Sci. 2020, 10(2), 551; https://doi.org/10.3390/app10020551 - 11 Jan 2020
Cited by 3 | Viewed by 1304
Abstract
Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, [...] Read more.
Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Analysis of Decision Support System Based on 2-Tuple Spherical Fuzzy Linguistic Aggregation Information
Appl. Sci. 2020, 10(1), 276; https://doi.org/10.3390/app10010276 - 30 Dec 2019
Cited by 2 | Viewed by 609
Abstract
The aim of this paper is to propose the 2-tuple spherical fuzzy linguistic aggregation operators and a decision-making approach to deal with uncertainties in the form of 2-tuple spherical fuzzy linguistic sets. 2-tuple spherical fuzzy linguistic operators have more flexibility than general fuzzy [...] Read more.
The aim of this paper is to propose the 2-tuple spherical fuzzy linguistic aggregation operators and a decision-making approach to deal with uncertainties in the form of 2-tuple spherical fuzzy linguistic sets. 2-tuple spherical fuzzy linguistic operators have more flexibility than general fuzzy set. We proposed a numbers of aggregation operators, namely 2-tuple spherical fuzzy linguistic weighted average, 2-tuple spherical fuzzy linguistic ordered weighted average, 2-tuple spherical fuzzy linguistic hybrid average, 2-tuple spherical fuzzy linguistic weighted geometric, 2-tuple spherical fuzzy linguistic ordered geometric, and 2-tuple spherical fuzzy linguistic hybrid geometric operators. The distinguishing feature of these proposed operators is studied. At that point, we have used these operators to design a model to deal with multiple attribute decision-making issues under the 2-tuple spherical fuzzy linguistic information. Then, a practical application for best company selection for feeds is given to prove the introduced technique and to show its practicability and effectiveness. Besides this, a systematic comparison analysis with other existent methods is conducted to reveal the advantage of our method. Results indicate that the proposed method is suitable and effective for decision making problems. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
Open AccessArticle
Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection
Appl. Sci. 2019, 9(23), 5215; https://doi.org/10.3390/app9235215 - 30 Nov 2019
Cited by 1 | Viewed by 679
Abstract
With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and [...] Read more.
With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing
Appl. Sci. 2019, 9(22), 4748; https://doi.org/10.3390/app9224748 - 07 Nov 2019
Cited by 2 | Viewed by 771
Abstract
The classical approach to non-linear regression in physics is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging [...] Read more.
The classical approach to non-linear regression in physics is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterized by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built in the models to compensate for the difficulty of modeling things that are, by construction, difficult to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here, the MTL architecture allows predicting multiple parameters, the oxygen concentration and temperature, from a single set of measurements. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks
Appl. Sci. 2019, 9(19), 4028; https://doi.org/10.3390/app9194028 - 26 Sep 2019
Cited by 3 | Viewed by 596
Abstract
Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to [...] Read more.
Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Around View Monitoring-Based Vacant Parking Space Detection and Analysis
Appl. Sci. 2019, 9(16), 3403; https://doi.org/10.3390/app9163403 - 19 Aug 2019
Cited by 1 | Viewed by 1643
Abstract
Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the [...] Read more.
Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method’s detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Sensorless Air Flow Control in an HVAC System through Deep Learning
Appl. Sci. 2019, 9(16), 3293; https://doi.org/10.3390/app9163293 - 11 Aug 2019
Cited by 2 | Viewed by 1262
Abstract
Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the [...] Read more.
Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN). Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations
Appl. Sci. 2019, 9(15), 3015; https://doi.org/10.3390/app9153015 - 26 Jul 2019
Cited by 2 | Viewed by 870
Abstract
Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness [...] Read more.
Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness feature values. Unfortunately, the brightness is distorted by the target and background variations. This paper proposes a novel pedestrian tracking algorithm, thermal infrared mean-shift (TIR-MS), by introducing radiometric temperature data in mean-shift tracking. The thermal brightness image (eight-bits) was distorted by the automatic contrast enhancement of the scene such as hot objects in the background. On the other hand, the temperature data was unaffected directly by the background change, except for variations by the seasonal effect, which is more stable than the brightness. The experimental results showed that the TIR-MS outperformed the original mean-shift-based brightness when tracking a pedestrian head with successive background variations. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)
Appl. Sci. 2019, 9(15), 2983; https://doi.org/10.3390/app9152983 - 25 Jul 2019
Cited by 5 | Viewed by 1344
Abstract
There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in [...] Read more.
There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Review

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Open AccessReview
An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
Appl. Sci. 2020, 10(6), 1928; https://doi.org/10.3390/app10061928 - 11 Mar 2020
Cited by 2 | Viewed by 734
Abstract
The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to [...] Read more.
The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, there has been an outstanding interest in the application of new optimization approaches for dealing with the main drawbacks present in the early IR methods, e.g., the Iterative Closest Point (ICP) algorithm. In particular, nature-inspired computation, e.g., evolutionary computation (EC), provides computational models that have their origin in evolution theories of nature. Moreover, other general purpose algorithms known as metaheuristics are also considered in this category of methods. Both nature-inspired and metaheuristic algorithms have been extensively adopted for tackling the IR problem, thus becoming a reliable alternative for optimization purposes. In this contribution, we aim to perform a comprehensive overview of the last decade (2009–2019) regarding the successful usage of this family of optimization approaches when facing the IR problem. Specifically, twenty-four methods (around 16 percent) of more than one hundred and fifty different contributions in the state-of-the-art have been selected. Several enhancements have been accordingly provided based on the promising outcomes shown by specific algorithmic designs. Finally, our research has shown that the field of nature-inspired and metaheuristic algorithms has increased its interest in the last decade to address the IR problem, and it has been highlighted that there is still room for improvement. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessReview
Information Fusion for Multi-Source Material Data: Progress and Challenges
Appl. Sci. 2019, 9(17), 3473; https://doi.org/10.3390/app9173473 - 22 Aug 2019
Cited by 4 | Viewed by 1127
Abstract
The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework [...] Read more.
The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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