Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Design of real-time data compression wireless sensor network based on LZW algorithm

A real-time data compression wireless sensor network based on Lempel-Ziv-Welch encoding(LZW)algorithm is designed for the increasing data volume of terminal nodes when using ZigBee for long-distance wireless communication.The system consists of a terminal node,a router,a coordinator,and an upper computer.The terminal node is responsible for storing and sending the collected data after the LZW compression algorithm is compressed;The router is responsible for the relay of data in the wireless network;The coordinator is responsible for sending the received data to the upper computer.In terms of network function realization,the development and configuration of CC2530 chips on terminal nodes,router nodes,and coordinator nodes are completed using the Z-stack protocol stack,and the network is successfully organized.Through the final simulation analysis and test verification,the system realizes the wireless acquisition and storage of remote data,and reduces the network occupancy rate through the data compression,which has a certain practical value and application prospects.

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

Micro-failure process and failure mechanism of brittle rock under uniaxial compression using continuous real-time wave velocity measurement

In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.

Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty

Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

A hybrid quantum encoding algorithm of vector quantization for image compression

Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Heterogeneity induced strain localization in block-in-matrix-soils subjected to uniaxial loading using real-time CT scanning

Block-in-matrix-soils(bimsoils)are geological mixtures that have distinct structures consisting of relatively strong rock blocks and weak matrix soils.It is still a challenge to evaluate the mechanical behaviors of bimsoils because of the heterogeneity,chaotic structure,and lithological variability.As a result,only very limited laboratory studies have been reported on the evolution of their internal deformation.In this study,the deformation evolution of bimsoils under uniaxial loading is investigated using real-time X-ray computed tomography(CT)and image correlation algorithm(with a rock block percentage(RBP)of 40%).Three parameters,i.e.heterogeneity coefficient(K),correlation coefficient(CC),and standard deviation(STD)of displacement fields,are proposed to quantify the heterogeneity of the motion of the rock blocks and the progressive deformation of the bimsoils.Experimental results show that the rock blocks in bimsoils are prone to forming clusters with increasing loading,and the sliding surface goes around only one side of a cluster.Based on the movement of the rock blocks recorded by STD and CC,the progressive deformation of the bimsoils is quantitatively divided into three stages:initialization of the rotation of rock blocks,formation of rock block clusters,and formation of a shear band by rock blocks with significant rotation.Moreover,the experimental results demonstrate that the meso-motion of rock blocks controls the macroscopic mechanical properties of the samples.

A Class of Fast Algorithms in Real-Time Simulation

A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are given. Using the class of algorithms, evaluation for the right-hand side function is needed once in every integration-step. Moreover, comparing with the other methods with the same amount of work, their numerical stability regions are larger and the method errors are smaller, and the numerical experiments show that the algorithms are very effective.

Design of quantum VQ iteration and quantum VQ encoding algorithm taking O（√N） steps for data compression

Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N） steps of distance computing between two vectors. The quantum VQ iteration and corresponding quantum VQ encoding algorithm that takes O（√N） steps are presented in this paper. The unitary operation of distance computing can be performed on a number of vectors simultaneously because the quantum state exists in a superposition of states. The quantum VQ iteration comprises three oracles, by contrast many quantum algorithms have only one oracle, such as Shor＇s factorization algorithm and Grover＇s algorithm. Entanglement state is generated and used, by contrast the state in Grover＇s algorithm is not an entanglement state. The quantum VQ iteration is a rotation over subspace, by contrast the Grover iteration is a rotation over global space. The quantum VQ iteration extends the Grover iteration to the more complex search that requires more oracles. The method of the quantum VQ iteration is universal.

Application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning

This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively.

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

A Class of Parallel Algorithms of Real-TimeNumerical Simulation for Stiff Dynamic System

In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these methods are discussed. A-stable real-time parallel formula of two-stage third-order and A(α)-stable real-time parallel formula with o ≈ 89.96° of three-stage fourth-order are particularly given. The numerical simulation experiments in parallel environment show that the class of algorithms is efficient and applicable, with greater speedup.

Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method

Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning.

Dynamic constraint and objective generation approach for real-time train rescheduling model under human-computer interaction

Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.

A Mathematical Model of Real-Time Simulation and the Convergence Analysis on Real-Time Runge-Kutta Algorithms

In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.

Real-time Design Constraints in Implementing Active Vibration Control Algorithms

Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.