Search

Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of fitting complicated equations, which makes it widely used in the research of model identification. However, it only brings a black-box model where the design schemes based on an analytical model cannot be applied. In this article, a simple and novel scheme for modeling and control of robotic manipulators is proposed; without prior knowledge, a dynamic model in an analytical form is obtained from artificially excited training data using the symbolic regression technique, and then, a controller is designed based on the dynamic model. Due to the ingenious experimental design, on one hand, the amount of training data is far less than the system identification method by machine learning. On the other hand, a decoupling feature is used in the model that greatly simplifies controller design. The experimental results on two-degree of freedom (DOF) and 6-DOF robotic manipulator simulators verify that the scheme is feasible and effective.

The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (1). Adapted motor plans are consolidated into motor memories that contribute to better performance on re-exposure to the same dynamic condition. In parallel, dynamic perturbations can be compensated for by alternate motor control processes, such as co-contraction, that contribute to error reduction (2). Whether these control strategies share the same neural resources for memory formation is unclear. To address this gap in knowledge, we used a novel fMRI-compatible wrist robot, the MR-SoftWrist (3), to identify neural processes specific to dynamic adaptation and subsequent memory formation. Using the MR-SoftWrist, we acquired fMRI during a motor performance and a dynamic perturbation task to localize brain networks of interest. Resting state fMRI scans were acquired immediately before and after task performance to quantify changes in resting state functional connectivity (rsFC) within these networks. Twenty-four hours later, we assessed behavioral retention of training. A variance decomposition analysis was used to isolate behavior associated with adaptation versus alternate error reduction strategies. Immediately after the dynamic perturbation task, rsFC significantly increased within the corticothalamic-cerebellar network of the trained wrist and decreased interhemispherically within the cortical sensorimotor network. These changes were associated to behavioral measures of initial acquisition and retention, indicative of memory formation. Variance decomposition analysis revealed that increases within the cortico-thalamic-cerebellar network were associated with adaptation, while interhemispheric decreases in rsFC within the sensorimotor network were associated with alternate error reduction processes.Significance StatementMotor memory formation processes have been studied using resting state functional connectivity (rsFC) before and after exposure to adaptation tasks. However, due to technical limitations, previous studies have not investigated rsFC within brain regions localized during task execution, nor quantified rsFC within minutes after task performance. The present study used an fMRI-compatible wrist robot to localize relevant brain regions during dynamic adaptation within the cortico-thalamic-cerebellar network and bilateral sensorimotor network. Immediately following dynamic adaptation, we measured significant changes in rsFC within these networks, that were associated with adaptation behavior and with gains in behavior assessed 24 hours later, indicative of memory formation. Variance decomposition analysis identified distinct rsFC networks associated with adaptation specific processes, and with alternate motor control strategies.

Soil tillage is the most energy-intensive and costly process. Therefore, the creation of combined machines that perform multiple operations in one workflow is a pressing issue for today, the solution of which is inextricably linked to the integration of agricultural operations into one technological process that brings economic, organizational and agrobiological benefits. Based on the peculiarities of soil preparation for planting or planting crops as an object, and the need for further scientific design of the combined technological process of soil application of fertilizers, active crumbling and cultivation of root, plant residues and fertilizers as a subject, the purpose of scientific research analysis of the interaction of the rotor with the reservoir, determination and optimization of the basic parameters of knives, radius, rotational speed with a view to providing classic soil cutting and mixing with fertilizers. At State Agrarian and Engineering University in Podiya the design of the combined plow was developed. The technological process of their work combines the operations of deep plowing, the active grinding of the formation and the earning of root and crop residues, as well as the introduction of mineral fertilizers. As a result of researches, the angles of installation of the knife with respect to the radius of the rotor α, the angle of sharpening γ, the cutting angle ψ, the occipital angle ζ, the angle of conicity of the rotor β, the angle of displacement of the blades (rays) of the attachment, the magnitude of the transformation of the angle of sharpening, the analysis of the part of the sharpening , and according to its results the required speed of rotation at which the soil passes on the surface of the knife without loading is determined and the effect of its unloading is provided

Theoretical studies of the milling tillage working body, in particular, dynamic properties under conditions of periodic external load, are substantiated and the main technological parameters are substantiated, and energy performance indicators are analyzed. The following data were adopted as the initial data for theoretical studies of the milling working body: the size of the row-spacing and strips, the dimensional characteristics of the root and stem residues. Theoretical studies of the milling machine gave such justified results: the diameter of the milling drum Dfr.bar = 0.3 m, the rotation frequency of the milling drum nfr.bar = 190 ... 430 hv-1, the number of installed knives on one disk n = 4 pcs, the rotating speed Vob = 6.59 m/s. Rational parameters and operating modes are justified: milling power Nfr = 19.3 kW, torque on the milling drum shaft Mkr = 0.45 kN·m. Theoretically substantiated milling working body is implemented in the development of a combined grinder of plant residues of thick-stem crops. The investigated working body, which performs the technological process of grinding compacted plant residues of thick-stemmed crops with the proposed technology, will allow us to further substantiate in more detail the mathematical model of the combined method of processing the field clogged with plant residues and determine the structural and technological structure of the grinder. The further development of the theoretical foundations of grinding plant residues and soil, in particular milling working bodies used in combined units, has been obtained

The extrusion process of plastic transformation involves a series of variables and components which must be associated with the concept of reliability to achieve an expected performance in operational and quality terms. This paper describes the reliability framework of an extruder of a packaging company located in Brazil through the following mathematical representations: probability density function f(t), reliability curve R(t) and function rate of failure h(t). Through a statistical software the set of representations was able to develop the reliability framework of the machine, which can be used to support a FMEA analysis. Besides that, it was evidenced that in the current operating conditions of the machine, approximately 50% of the failure observations happen on the hour 100 of operating time. The main outcome is that it is recommended a systematic preventive maintenance based on 100h cycles. It is expected that this work may act as a guide for future implementation of improvement activities.

Face parsing aims to assign pixel-wise semantic labels to different facial components (e.g., hair, brows, and lips) in given face images. However, directly predicting pixel-level labels for each facial component over the whole face image would obtain limited accuracy, especially for tiny facial components. To address this problem, some recent works propose to first crop tiny patches from the whole face image and then predict masks for each facial component. However, such cropping-and-segmenting strategy consists of two independent stages, which cannot be jointly optimized. Besides, as one valuable piece of information for parsing the highly structured facial components, context cues are not elaborately explored by the existing works. To address these issues, we propose a component-level refinement network (CLRNet) for precisely segmenting out each facial component. Specifically, we introduce an attention mechanism to bridge the two independent stages together and form an end-to-end trainable pipeline for face parsing. Furthermore, we incorporate the global context information into the refining process for each cropped facial component patch, providing informative cues for accurate parsing. Extensive experiments are carried out on two benchmark datasets, LFW-PL and HELEN. The results demonstrate the superiority of the proposed CLRNet over other state-of-the-art methods, especially for tiny facial components.

There is in-depth understanding of the effects and interactions of various process parameters on the mechanical properties and dimensional accuracy of parts produced through fused filament fabricat...

This article investigates the leader-follower formation learning control (FLC) problem for discrete-time strict-feedback multiagent systems (MASs). The objective is to acquire the experience knowledge from the stable leader-follower adaptive formation control process and improve the control performance by reusing the experiential knowledge. First, a two-layer control scheme is proposed to solve the leader-follower formation control problem. In the first layer, by combining adaptive distributed observers and constructed $i_{n}$ -step predictors, the leader's future state is predicted by the followers in a distributed manner. In the second layer, the adaptive neural network (NN) controllers are constructed for the followers to ensure that all the followers track the predicted output of the leader. In the stable formation control process, the NN weights are verified to exponentially converge to their optimal values by developing an extended stability corollary of linear time-varying (LTV) system. Second, by constructing some specific ``learning rules,'' the NN weights with convergent sequences are synthetically acquired and stored in the followers as experience knowledge. Then, the stored knowledge is reused to construct the FLC. The proposed FLC method not only solves the leader-follower formation problem but also improves the transient control performance. Finally, the validity of the presented FLC scheme is illustrated by simulations.

Finding optimal parameters for acceleration structures for raytracing is key to improved performance. Previous research has shown that a speedup of over 10% of rendering time is possible. Some parameters are interdependent which complicates the process of finding an optimal configuration. It is hence interesting to find them efficiently. Autotuning is an automatic optimization scheme able to search for optimal configurations and has been applied successfully to kD-trees in the past, which we apply today on BVHs. The more parameters to optimize, the more difficult it is to find optimal solutions. In this paper, we analyze in detail the behavior of the parameters and their impact on acceleration structure building and rendering time. We show the interdependence and context sensitivity (i.e., scene, viewpoint) of the parameters. Based on the use case, this allows to target only crucial parameters. Convergence speed towards an optimal configuration is essential. To find better parameters, the autotuner needs to build the acceleration structure over and over, changing parameters every time. We introduce a hybrid model-based prediction and online autotuning method to address this issue. The prediction model allows for both instantaneous near-optimal configurations when inputs are known or similar, and efficient search of the configuration space when inputs are completely new. Online autotuning outperforms configurations recommended in literature by up to 11% median. The prediction model achieves 95% of the maximum speedup of the autotuner while reducing 90% of its overhead. Thus, hybrid online autonuning enables always-on tuning in ray tracing.

The unconventional machining processes are popular in machining the difficult to cut type complex contour/ profile. The key parameters are to be machined with care as they directly affects the function and non key dimensions are machined with other than consideration of functional aspect, like design, passage. That means accuracy is less important in those dimensions but finish is to be good. So the machining of engineering materials does not always require optimizing parameters based on multiple responses. This investigation focuses on the sensitiveness of Wire EDM of 12X18H10T - Stainless Steel and identifying the parameters contribution and establish mathematical model to predict values without trial. An austenitic alloy 12X18H10T - Stainless Steel exhibits its inherent mechanical properties even when it expose to extremely high temperature. Ithas potential application in aerospace engine components manufacturing. This investigation utilized the Taguchi L9 experiential Design with factors of Pulse off Time, Wire Tension, Pulse on Time, and Wire Feed Rate. This study aims to examine Maximize the rate of machining with Taguchi approach in the process of Wire EDM of 12X18H10T - Stainless Steel and establish a mathematical model to predict the highly influencing parameter(s) for desired rate of machining.

Dye-Sensitized Solar Cells (DSSCs) has been extensively explored as a third generation solar cell due to their simple fabrication procedures, better performance especially under low-light intensities, aesthetic features, etc. DSSCs provide an efficient and simple effectuated technology for prospective energy demands. Computing the experimental data in a high performance platform is really an appreciable task for better understanding and accessibility for photovoltaic technology. In this research paper, we fabricate the proposed DSSC, process the experimental data, as an alternative method we compute the experimental data and obtain I-V and IPCE characteristics by programming in MATLAB.

The recognition a material quality is considered as a process of finding out the constituent material present in an object and it is regarded as a vital part in various applications. Hence, it is considered as a valuable approach for the creation of a system that possesses the ability to achieve recognition of a material. In this paper, we develop a mechanism using convolutional neural networks (CNNs) for material recognition. The CNN model initially trains itself with the features extracted from the image samples. Finally, the classification is carried out with CNN model that learn the classes obtained via CNN of different category of materials. The experimental validation is conducted to test the accuracy of CNN classifiers against various deep learning classifiers. The results on various materials show that the proposed CNN classifier obtains improved recognition accuracy than other methods.

Ship-hull design is a complex process, because the small local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship. To find the optimum hull shape under the design requirements, the state-of-art of ship hull design combines computational fluid dynamics computation with the geometric modeling. However, this process is very computationally intensive, which is only suitable at the final stage of the design process. To narrow down the design parameter space, in this work we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process. In this work, we have demonstrated how to use the developed DNN model to carry out the initial ship hull design. The validation results showed that the deep learning model can accurately predict the ship hull’s total resistance accurately after being trained, where the average error of all samples in the testing dataset is lower than 4%. Simultaneously, the trained deep learning model can provide the information about a ship’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes. The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.

Selective laser melting (SLM), an additive manufacturing technology, is expected to replace the traditional lost-wax casting process used in producing removable partial denture (RPD) frameworks. However, studies comparing the accuracy of RPD frameworks and the effects of process parameters are lacking.The purpose of this in vitro study was to optimize SLM process parameters and use a quantitative analysis method to improve the accuracy of 3D-printed RPD frameworks.The orientation and support structure of Kennedy Class II RPDs were designed in various ways by using 2 different software programs, CAMbridge and Magics. The optimum melt-pool parameters, including laser power, scan speed, hatch distance, and layer thickness, were determined empirically before manufacturing 12 RPD frameworks with 4 different process designs by using SLM (n=3). The accuracy of the RPD frameworks was determined by 3D scanning and comparing the 3D scan data with the original standard tessellation language (STL) RPD design with the best-fit algorithm of the Geomagic software program.Optimum melt-pool parameters were found with the function of density, surface roughness, and productivity (P=180 W, v=1200 mm/s, h=60 μm, t=30 μm). RPD frameworks fabricated by the optimized process parameters (167 ±105 μm) showed significantly better (P.05) mean ±standard deviation accuracy than the 3 other groups of RPD frameworks manufactured by using the nonoptimized process parameters (180 ±121 μm to 222 ±136 μm). The best accuracy was found with the transverse orientation and interconnected support structure.With the optimized design of process parameters, clinically acceptable RPD frameworks were produced. The accuracy of RPD frameworks fabricated by using SLM varied according to the design of the process parameters, indicating that SLM technology can replace the traditional lost-wax casting process.

Edge detection is a basic problem in computer vision and image processing. The main purpose of edge detection is to identify points with obvious brightness changes in digital images. At present, there are many good detection methods, but most of them do not consider the correctness and crispness of edges at the same time. In order to address this problem, this paper proposes a method based on a deep convolution neural network. The method is mainly based on a dense network that is then combined with the single network structure of a backward refinement path module. The former can detect and retain the feature information between different layers in an image. The latter makes full use of the extracted information so that the low-level detail features and high-level abstract features can be better integrated in the final output. We tested the method on the BIPED data set. The results show that the correctness and crispness of the edges can be balanced in the detection process, and the ODS, OIS and AP of this method reach 0.888, 0.893 and 0.916, respectively. Compared with the state-of-the-art approaches, the proposed method improves the standard evaluation by 3%-5%, and the convergence speed is also significantly improved.

The tool and die applications are preferred the unconventional machining processes as they are popular in machining the difficult to cut type complex contour/ profile. The complex profile machined on hard material. Some special materials like Stainless Steel 12X18H10T withstand its inherent properties even it exposes at higher temperature. This investigation aims to expand its application by investigating its machinability in Electro Chemical machining process for surface finish. The process parameters like gap between work material and tool, discharge current, concentration of electrolyte and applied voltage at three levels. The Taguchi approach is used for optimizing the process parameters and confirmation runs also conducted for developing the mathematical modelling. For optimizing number of experiment the Taguchi L9 is utilized for this investigation.

The article studies Automatic Oil Spraying Machine design and manufacture for baking device. In a current baking process, the workers manually spray the oil onto the trays. The spraying cycle increases the cost of man power in small scale industries. An automated oil spraying system is designed to solve these issues, where they can automatically spray the oil on the baking tray easily and effectively. The main components of this device are the mechanical and electrical components. The mechanical portion consists of conveyor belt, roller, bearing, gear, nozzle and tank, and mechanical structure. The electrical components include the IR sensor module, the dc motor, the dc pump, Arduino, and the source of the batteries. The key advantages of this approach are high precision oil spraying; improve efficiency, cost reduction and manpower. This model system's gross weight contains 15 kg (roughly). The efficiency of this device is checked for product production under different working conditions to track the performance of the work and the time durations.

The basic polite NLMS adaptive channel calculation is ordinarily utilized in applications where a framework needs to adjust to its condition. Structures are analyzed as far as the accompanying criteria: speed, power utilization and FPGA utilized. Present day FPGAs contain numerous assets that help signal processing applications. This posits are actualized in the programmable ICs texture and enhanced for superior and less power consumption The LMS adaptive finite impulse channelsarevery well known adaptive calculation process and is probably going to keep on so within a reasonable time-frame. In spite of theories that the LMS calculation is losing it is built up status as the workhorse for the plan of straight adaptive frameworks, there are as yet various continuous looks into and best in class propels right now. An expected new book titled — Least Mean-Square Adaptive Filters, altered by Bernard Widrow (originator of LMS) and Simon Haykin is a decent delegate of gave interests right now appeared by scientists around the globe. The LMS calculation can be effectively altered to a standardized advance size adaptation known as the Normalized LMS (NLMS) calculation. NLMS, gives a possibly quicker adaptive calculation, yet in addition ensures an increasingly steady combination in light of varieties of input signal power. Following this methodology, the fundamental goals of this postulation is to execute the NLMS-based adaptive calculation for awardee framework in outlaying communication to show its great gathering figure and reduce the error. Fartheriproposedone more design for DFS consumption it transfer about the usage of reduced memory use. The fundamental downside of the “absolute” least mean square computation this one touchy to the measuring the original signal X(N). It creates a difficult to pick a known rate µ that ensures good measurement. This is very useful for lowering the power consumption.

Density-Based clustering are the main clustering algorithms because they can cluster data with different shapes and densities, but some of these algorithms have high time complexity like OPTICS (Ordering Points to Identify Clustering Structure) and DBSCAN (Density-based spatial clustering of applications with noise) where the time complexity up to O( n 2 ). In this paper, we use an approach to reduce this complexity by providing fuzzy clusters to OPTICS, which make the process of finding neighbours within an only narrow region (fuzzy group) instead of searching all the state space making the algorithm faster than the original one with keeping almost the same accuracy of the innovative algorithm. The results show that there is an improvement in execution time using some synthetic and real datasets with high dimensions.

Now days, the human being are affecting by so many diseases in all over the world. If we will consider about the tumor in the encephalon, then detection of tumor often involves radiological imaging. Radiological Imaging is used to check the spread of tumor and progress of treatment and also used to monitor cancer. The manual detection of the Encephalon tumor becomes a meticulous and chaotic task for the clinical experts. If the doctors or various departmental specialists want to eliminate the spoiled cancer areas from Magnetic Resonance Images, then it becomes very hectic and takes more time. The main aim of this work is to detect the encephalon tumor regions. Once the brain is going to be scan then it offers proficient and quick identification of encephalon tumor. To modernize the performance and abbreviate the intricacy involved in the image detection process in the proposed system as well as the Fuzzy CMean predicated image segmentation processes are used. Furthermore, to expand the variance of different images, particularly when the data are worn in image is signified beside different variance of images and then another technique has been implemented in the paper that is Histogram Equalization. The proposed method is completely automatic and tested on different kinds of brain MR images and proved as robust.

Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by modeling correlation among labels. In previous methods, label correlation is computed based on statistical information through label diffusion, and therefore the same for all samples. This, however, makes graph inference on labels insufficient to handle huge variations among numerous image instances. In this paper, we propose an instance-aware graph convolutional neural network (IA_GCN) framework for the multi-label classification. As a whole, two fused branches of sub-networks are involved in the framework: a global branch modeling the whole image and a local branch exploring dependencies among regions of interests (ROIs). For both the branches, an image-dependent label correlation matrix (ID_LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed to inject adaptive information of label-awareness into the learned features of the model through graph convolution. Specifically, the individual LCM of each image is obtained by mining the label dependencies based on the predicted label scores of those detected ROIs. In this process, considering the contribution differences of ROIs to multi-label classification, variational inference is introduced to learn adaptive scaling factors for those ROIs by considering their complex distribution. Finally, extensive experiments on MS-COCO and VOC datasets show that our proposed approach outperforms existing state-of-the-art methods.

SYN Flood Attack is one form of distributed denial of service attack that attains the handshake process of TCP. This attack consumes all available server resources and provokes legitimate traffic which aims to make the server unavailable. It causes serious damage to cloud server and networking protocols. The main objective of this research work is to train the neural network for detecting the attack and to secure network connection. A novel binary fruit fly optimization algorithm with deep learning is proposed to predict the syn flood attack. The proposed algorithm is implemented using the KDD cup dataset. DL- BFFA algorithm has achieved 99.96% detection accuracy for detecting the SYN Flood Attack. A comparison study is conducted to validate the proposed model.

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

The Micro-expressions (MEs) are very important nonverbal communication clues. However, due to their local and short nature, spotting them is challenging. In this paper, we address this problem by using a dedicated local and temporal pattern (LTP) of facial movement. This pattern has a specific shape (S-pattern) when ME are displayed. Thus, by using a classical classification algorithm (SVM), MEs are distinguished from other facial movements. We also propose a global final fusion analysis on the whole face to improve the distinction between ME (local) and head (global) movements. However, the learning of S-patterns is limited by the small number of ME databases and the low volume of ME samples. Hammerstein models (HMs) are known to be a good approximation of muscle movements. By approximating each S-pattern with a HM, we can both filter outliers and generate new similar S-patterns. By this way, we perform a data augmentation for S-pattern training dataset and improve the ability to differentiate micro-expressions from other facial movements. The spotting results, performed on CASME and CASME, show that our proposed LTP outperforms the most popular spotting method in terms of F1-score. Adding the fusion process and data augmentation improve even more the spotting performance.

In the process of digital image acquisition, color inconsistency is a common issue due to the influence of photographic conditions. The inconsistency of color affects the quality of digital orthophoto products and subsequent applications significantly, and this issue remains challenging both in the academia and industry. This paper proposes a color equalization method for aerial image based on block adjustment and an improved color balance model based on the analysis of causes for color deviation. Two nonlinear functions are used to simulate the process of color deviation and correct the color of images. Due to the existence of error, the color corrected images still have minor deviations in the color of the overlapped regions. Therefore, this research introduces an error equation to address this issue and uses the least square principle to calculate the adjustment. The simulation experiments and real experiments are carried out on several groups of aerial image data respectively. The results show that the method not only achieves better visual effect but also has a higher adjustment precision. Finally, the rationality and parameters of the model proposed in this paper are discussed.

Most existing light field saliency detection methods have achieved great success by exploiting unique light field data-focus information in focal slices. However, they process light field data in a slicewise way, leading to suboptimal results because the relative contribution of different regions in focal slices is ignored. How we can comprehensively explore and integrate focused saliency regions that would positively contribute to accurate saliency detection. Answering this question inspires us to develop a new insight. In this article, we propose a patch-aware network to explore light field data in a regionwise way. First, we excavate focused salient regions with a proposed multisource learning module (MSLM), which generates a filtering strategy for integration followed by three guidances based on saliency, boundary, and position. Second, we design a sharpness recognition module (SRM) to refine and update this strategy and perform feature integration. With our proposed MSLM and SRM, we can obtain more accurate and complete saliency maps. Comprehensive experiments on three benchmark datasets prove that our proposed method achieves competitive performance over 2-D, 3-D, and 4-D salient object detection methods. The code and results of our method are available at https://github.com/OIPLab-DUT/IEEE-TCYB-PANet.

There are some factors that play a crucial role in deciding the image quality during the optical as well as the micrographic association with the technique of the shadow casting in which contrast of the image has influenced with obliquely depositing of a thin layer of the metal during the process of the microscopic preparations. Further castingadvantages of the using shadow technique are to be observed during the process if shadow casting itself. Shadow casting posses’ number of the advantages over the other casting methods. The most prominent advantage is that if the height and thickness of an object is not completely flat that can be easily notice by the shadow of that object. The lengths of the shadow of the uneven part are so differentiate from other part that can be easily determined. Shadow casting uses the scattering power or even the optical efficiency to bring out the specimens contours and will easily get notice in order to check the flatness of the surfaces.

This paper highlighted on a new improvement of the salp swarm algorithm (SSA) that is taken from the behavior of salp fishes. This shows a promising result, however it suffered from some drawbacks that limited its ability. The proposed improvement of SSA is called SSA 2. In this paper the design system where the ZigBee protocol received temperature, humidity and gas from the sensors. The base station collects received data and sends it to platform in order to process these data via SSA2. This system enables the ability to monitor and track any fault that may be occurring. A new term has been developed to make the algorithm more targeted in finding the optimal answer. Moreover, the calculation results and simulation results in Matlab shows great enhancement in the accuracy and precision of the results at very fast speed for convergence compared with the original salp swarm algorithm (SSA).

The growth of transferring data via networks and the internet yield to safe them through communication. The image encryption process is a valuable strategy for protecting image information and it is used as an effective way of communicating several sensitive data with continuous use of images in all forms of visual media. In this paper, an efficient method of image encryption is proposed based on PRESENT-SPECK algorithms with a hybrid method for key generation called Henon and Arnold-cat map that is suggested for sequences. The proposed method petitioned the image data into the block for encryption. The total blocks joined and produced an encrypted image. The proposed method tested with various types, and the generated key also have passed various tests.

Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera).

The randomized treatment allocation process in a response adaptive clinical trial is formulated as a stochastic sequential decision problem and an algorithm is proposed to approximate the optimal value under the average reward criterion. When the information of previous treatment allocations and associated responses are summarized with sufficient statistics for unknown parameters, the decision process becomes a Markov process, on which a span-contractor operator is defined. It is proven that the average reward under the policy identified from the span-contractor operator converges almost surely to the optimal value. Numerical results reveal that the sequential procedure based on the controlled Markov process shows superior ethical advantage and at the same time produces good statistical power for large sample sizes such as 200 or larger.

Deep learning is the extension of machine learning technique which attracted the researchers in the recent past and hence the literature is also limited. This research focused on the need for deep learning for effective prediction process, when compared with few other machine learning models. As a proof of concept, a dataset of students’ performance of a school is analysed by various machine learning models. The performance measurements by all such models are compared with deep learning. These are assessed by two familiar statistical measures, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The error rates are low for deep learning when compared with other models. It is been evident that the result obtained by the deep learning model is more effective than the other machine learning models. The results are improved by reducing the error rates at the rates of 35% by mean absolute error and 48% by root mean squared error.

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process.ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.Contactpoh.chuehloo@nus.edu.sg

The job sequencing and tool switching (SSP) is a well-known combinatorial optimization problem that aims to minimize the number of tool switches. The problem mainly arises in a flexible manufacturing system, in which machines can be configured with various tools to process different jobs and tool switches will correspond to a reduction of productivity. While the general studies assume uniform setup time for tool switching, applications on the industries indicate that the setup time of a tool might depend on the previously installed tool of the same slot. Therefore, this article proposes two integer linear programming models for the sequence-dependent SSP, namely the five-index formulation and the multicommodity flow formulation. Experimental tests are executed to compare the sequence-dependent SSP with the uniform SSP and the results show the effectiveness of multicommodity flow formulation. Finally, several managerial discussions are derived from the results.

When agricultural goods begin to be transported by those temperatures where value added products can be transported, cold room assessments in industrial applications must be validated. Computational feedback can improve the process consistency. Data on coolant and boundary dynamics can be evaluated by means of computer fluid dynamics (CFD). Air speed and air temperature are the distribution parameters in this analysis. When the vegetable temperature is preserved with less variations in the storage room, as well as the required air distribution, the best effects of proper preservations are achieved. With proper air circulation, temperature difference is minimised. Air speed is necessary to protect the cold storage of agricultural products. This project's main objective was to observe the decrease in temperature when PCMs are used within the cold storage space. Most of the experimental results demonstrated that in the event of regular openings of doors, also in the context of electricity failures using PCM, the temperature retained inside the cool space was 1–4.5 °C lower than without PCM, even in the cases of electrical failures using PCM. The present study focuses on applying guidelines to enhance cold room technology for improved outcomes in cold storage. Present work discussed about the reference methods and the techniques used in previous literature with future recommendations.

The technological innovation among a conscience as well as configured accessibility guidance conceptual model process has been combined mostly with digital technologies that generate integrated immediate transportation with adaptability shielding mechanism. The Intelligent as well as Adaptive Accessibility Benefits Interface updates and transmits roadway data collected by roadside sensors to a powerful guide known as a dynamic map (DM), which increases programmed portability control with high accuracy and reliability. To confirm the updating and establishment of roadway data to automatic motilities, we have built up a Wireless Electronic Traffic Mirror (WETM) that procures street data, perceives street items and moves acknowledgment data with a remote correspondence framework. Furthermore, to meet the security prerequisites, the AMAS should give the remote association most appropriate for portability. To give ideal remote availability, continuous remote channel checking, which quantifies the nature of remote channels with obstruction, is a significant assignment in the AMAS. The continuous peripheral distract surveillance implementation throughout combination with the generated WETM is mentioned in this report, and unambiguous confirmation including its exactness of every situation occurrence is revealed to explain the thinking beside how some the phenomenon including its pavement was improved.

In this article, a combined gradient descent-Barzilai Borwein (GD-BB) algorithm and radial basis function neural network (RBFNN) output tracking control strategy was proposed for a family of nonlinear systems with unknown drift function and control input gain function. In such a method, a neural network (NN) is used to approximate the controller directly. The main merits of the proposed strategy are given as follows: first, not only the NN parameters, such as weights, centers, and widths but also the learning rates of NN parameter updating laws are updated online via the proposed learning algorithm based on Barzilai-Borwein technique; and second, the controller design process can be further simplified, the controller parameters that should be tuned can be greatly reduced. Theoretical analysis about the stability of the closed-loop system is manifested. In addition, simulations were conducted on a numerical discrete time system and an inverted pendulum system to validate the presented control strategy.

In Intelligent Transportation Systems, images are the main data sources to be analyzed for providing intelligent and precision cognitive services. Therefore, how to protect the privacy of sensitive images in the process of information transmission has become an important research issue, especially in future no non-private data era. In this article, we design the Rearrangement-Arnold Cat Map (R-ACM) to disturb the relationship between adjacent pixels and further propose an efficient Bit-level Image Encryption Algorithm(Bl-IEA) based on R-ACM. Experiments show that the correlation coefficients of two adjacent pixels are 0.0022 in the horizontal direction, -0.0105 in the vertical direction, and -0.0035 in the diagonal direction respectively, which are obviously weaker than that of the original image with high correlations of adjacent pixels. What's more, the NPCR is 0.996120172, and the UACI is 0.334613406, which indicate that Bl-IEA has stronger ability to resist different attacks compared with other solutions. Especially, the lower time complexity and only one round permutation make it particularly suitable to be used in the time-limited intelligent transportation field.

This paper introduces optimization of machining parameters for high-pressure abrasive water jet cutting of Hardox 500 steel utilizing desirability function analysis (DFA). The tests were carried out according to the orthogonal matrix (Taguchi) L9. The control parameters of the process such as pressure, abrasive flow rate, and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the basis of the composite desirability value obtained from desirability function analysis and the significance of these parameters was determined by analysis of variance (ANOVA). The effects show that optimal sets for high cutting depth and small surface roughness is high pressure, middle abrasive flow rate, and small traverse speed. A confirmation test was also leaded to validate the test results. Results of the research have shown that machining efficiency at keeping good level quality of cut surface can be improved this approach.

Federated learning (FL) is an emerging machinelearning framework, which enables multiple mobile users to collaboratively train a global model without uploading their local sensitive data. Due to limited network bandwidth, communication efficiency has become a significant bottleneck for the implementation of FL. Existing works attempt to improve this situation by reducing the total bits transferred for each client update via data compression. However, these research works are only from the perspective of update parameters to reduce the transmission of redundant parameters. They do not explore the intrinsic reasons for redundant parameters. In this article, we propose a coreset-based FL (CBFL) framework. Instead of training model on full datasets with a regular network model, CBFL uses a much smaller well-matched evolutionary network model on coreset. CBFL indirectly reduces the total transmission bits for each client while achieving a similar accuracy as training with full datasets. CBFL includes a novel distributed coreset construction and adaptive model evolution algorithms. The network model is adaptively adjusted during this process, which dynamically removes the least important connections from the current model. Experimental results with various datasets and models show that CBFL is able to find an optimized evolutionary model that has about 10% of the total number of connections in the original regular model while only about 2% degradation in model accuracy.

In this article, a novel composite hierarchical antidisturbance control (CHADC) algorithm aided by the information-theoretic learning (ITL) technique is developed for non-Gaussian stochastic systems subject to dynamic disturbances. The whole control process consists of some time-domain intervals called batches. Within each batch, a CHADC scheme is applied to the system, where a disturbance observer (DO) is employed to estimate the dynamic disturbance and a composite control strategy integrating feedforward compensation and feedback control is adopted. The information-theoretic measure (entropy or information potential) is employed to quantify the randomness of the controlled system, based on which the gain matrices of DO and feedback controller are updated between two adjacent batches. In this way, the mean-square stability is guaranteed within each batch, and the system performance is improved along with the progress of batches. The proposed algorithm has enhanced disturbance rejection ability and good applicability to non-Gaussian noise environment, which contributes to extending CHADC theory to the general stochastic case. Finally, simulation examples are included to verify the effectiveness of theoretical results.

Deep-learning-based soft sensors have been extensively developed for predicting key quality or performance variables in industrial processes. However, most approaches assume that data are uniformly sampled while the multiple variables are often acquired at different rates in practical processes. This article designed a progressive transfer strategy, based on which a variational progressive-transfer network (VPTN) method is proposed for the soft sensor development of industrial multirate processes. In VPTN, the multirate data are first separated into multiple data chunks where the variables within each chunk are acquired at a uniform rate. Then, a variational multichunk data modeling framework is developed to model the multiple chunks in a unified fashion through deep variational structures. The base models, including the unsupervised ones with only partial process variables and the supervised soft sensor model share a similar network structure, such that the subsequent transfer strategy can be readily implemented. Finally, a progressive transfer learning strategy is designed to transfer the model parameters from the fastest sampled data chunk to the slowest one in a progressive manner. Thus, the knowledge from various data chunks can be sequentially explored and transferred to enhance the performance of the terminal soft sensor model. Case studies on both a debutanizer column dataset and a real coal mill dataset in a thermal power plant validate the performance of the proposed method.

Oil and gas industry continues to the increasing demand of more cost-effective well design and operations. Thus, team successfully eliminated intermediate casing string from the design thereby achieving not only a 33% reduction in dry hole cost/meter, but also delivering wells and producing oil 25% faster than before. Removing an intermediate casing string resulted in a longer open hole (more than 2000 mMD) which by default created new challenges which had to be dealt with and solved without compromising the targeted cost saving. The challenges can be summarized as follow: • Combining loss of circulation zone with unstable shale formations in one section. • PPFG uncertainties. • Bit selection and design optimization. • BHA selection and drive optimization. • Running wireline logging through a long open hole. • Running casing and having good quality cement. • Hole cleaning and displacement efficiency when changing mud systems. • Proper contingencies in place to meet the required completion strategy. Risk assessment, feasibility studies, workshops, and co-operation among all involved personnel has been conducted to ensure that this design optimization would result in positive outcome. Since the first implementation in 2019, more than 17 wells have been successfully delivered and completed in Best in Class performance based on latest update of Rushmore data tracking. The longest open hole section of 2790 m measured depth (MD) with total depth (TD) of 3910 mMD has been achieved generating above 800 Kusd saving per well, thus setting new benchmarks for the field and opening up new opportunities for the future. All this ensure Process Safety and compliance with Well Delivery process. This paper not only describes how two string design was successfully engineered and executed but also serves as a guide for selecting proper candidates for this design and an operational guide for two section wells design to ensure that these challenging long open hole will be successfully drilled while minimizing risks.

Video-based person re-identification (Re-ID) aims at retrieving the person through the video sequences across non-overlapping cameras. Some characteristics of pedestrians are not consecutive across frames due to the variations of viewpoints, postures, and occlusions over time. However, existing methods ignore such data peculiarity and the networks tend to only learn those salient consecutive characteristics among frames in video sequences. As a result, the learned representations fail to cover all the characteristics of pedestrians, thus lacking integrity and discrimination. To tackle this problem, we present a novel deep architecture termed Hierarchical Mining Network (HMN), which mines as many pedestrians’ characteristics by referring to the temporal and intra-class knowledge. It consists of a novel Attentive Temporal Module (ATM) and a Dynamic Supervising Branch (DSB), with a Balancing Triplet Loss (BTL) assisting the training. The proposed ATM, with pedestrian perceiving capacity, is capable of evaluating each activation of features through temporal analysis, so that the temporally scattered characteristics of pedestrians can be better aggregated and the contaminated ones can be eliminated. Then, the DSB along with the BTL further enhances the integrity of representations by multiple supervision. Specifically, the DSB perceives the diversities of intra-class samples in each mini-batch and generates targeted supervising signals for them, in which process the BTL guarantees the signals with smaller intra-class variations and larger inter-class variations. Comprehensive experiments on two video-based datasets, i.e., MARS, and DukeMTMC-VideoReID, demonstrate the contribution of each component and the superiority of the proposed HMN over the state-of-the-arts. Benchmarking our model on three popular image-based datasets, i.e., Market1501, DukeMTMC-Reid, and MSMT17 additionally verifies the promising generalizability of the proposed DSB and BTL.

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a global model with highly distributed data located on mobile devices. Aiming to optimize the communication efficiency for gradient aggregation and model synchronization among large-scale devices, we propose a relay-assisted FL framework. By breaking the traditional transmission-order constraint and exploiting the broadcast characteristic of relay nodes, we design a novel synchronization scheme named Partial Synchronization Parallel (PSP), in which models and gradients are transmitted simultaneously and aggregated at relay nodes, resulting in traffic reduction. We prove that PSP has the same convergence rate as the sequential synchronization approaches via rigorous analysis. To further accelerate the training process, we integrate PSP with any unbiased and error-bounded compression technologies and prove that the convergence properties of the resulting scheme still hold. Extensive experiments are conducted in a distributed cluster environment with real-world datasets and the results demonstrate that our proposed approach reduces the training time up to 37\% compared to state-of-the-art methods.

Real-world graphs exhibit many interesting properties that differentiate them from random graphs, which have been extensively studied for the past decades. For various proposed generative models, a majority of them build the graph by sequentially adding each node and the attached edges. However, the growth of many real-world graphs, such as social networks, is naturally modeled by the sequential insertion of edges. Unfortunately, to the best of our knowledge, no generative model has been proposed to reveal this process. We propose the first sequence-of-edges model, denoted as temporal preferential attachment (TPA). It relies on preferential attachment (PA), one of the most influential mechanisms to generate scale-free graphs, and takes time-decay effect and node fitness into consideration. Empirical analysis demonstrates that our model preserves several key properties of the real-world graphs, including both the properties observed from the snapshot graphs (e.g., power-law distribution) and temporal properties observed from the graph generation process (e.g., shrinking diameter). Meanwhile, our model is sufficiently general to accommodate several forms of time decay and fitness distributions. Then, we design two efficient algorithms that generate TPA graphs with billions of edges in several minutes.

Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on market performance by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we design four treatment groups with different choice capacities in which users can only interact with other users in the same group and randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs and generalize our findings by discussing how choice capacity can be designed to increase engagement and matching outcomes.