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.

Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.

Discussion on the Status Quo of Vocational College Students’Self-Image and its Improvement Strategies

An individual’s self-image is a multi-dimensional and multi-structural concept.Its internal dimensions include ability,knowledge,values,personality,and temperament,and its external dimensions are physical appearance,behavior,and clothing.A good image will have a positive impact both in life and at work.We will choose appropriate clothing and makeup to modify the external image and cultivate positive qualities such as correct values and an optimistic attitude towards life to enhance internal dimensions.Among them,“personality”and“ability”mostly belong to the research category of mental health education,and“values”fit in the research field of ideological and political education.Ideological and political education and mental health education are both important components of higher education,which show similarities between them.Ideological and political education and mental health education can complement each other in many ways to enhance students’self-image.

Design and Development an Interactive On-the-Job Training Monitoring and Help Desk System with SMS for College of Information and Communication Technology

This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the College of Information and Communication Technology Nueva Ecija University of Science and Technology. The system made the OJT course procedure trouble-free by emerging a system accessible through the internet. Students have a user account, which gives them the aptitude to upload document files of their reports, thereby minimizing the time and energy spent traveling from the company’s location to the university and the other way around. Similarly, the OJT coordinators of the college are given their accounts to access and check the reports submitted by the students. The system is capable of generating reports and requirements in real-time, as long as all data is stored within the database and, therefore, the process is completed online. In addition, the system provides an interactive website that might help both students and coordinators to communicate instantaneously by having an online help desk where the students can ask related questions on their OJT course that the OJT coordinator and other students will answer. The coordinators can send a brief message service to the students enrolled within the OJT course through the utilization of the proposed system - this can be for the students who aren’t capable of opening their account more often, in order that they are still informed of the announcements they need to understand immediately. The interactive OJT help desk system with SMS can be used as a tool to help the students of the College of Information and Communication Technology (CICT) and the OJT coordinators in their tasks more conveniently.

The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real environments.In this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

Asymptotic analysis on bright solitons and breather solutions of a generalized higher-order nonlinear Schrödinger equation in an optical fiber or a planar waveguide

We study a generalized higher-order nonlinear Schr¨odinger equation in an optical fiber or a planar waveguide.We obtain the Lax pair and N-fold Darboux transformation(DT)with N being a positive integer.Based on Lax pair obtained by us,we derive the infinitely-many conservation laws.We give the bright one-,two-,and N-soliton solutions,and the first-,second-,and Nth-order breather solutions based on the N-fold DT.We conclude that the velocities of the bright solitons are influenced by the distributed gain function,g(z),and variable coefficients in equation,h1(z),p1(z),r1(z),and s1(z)via the asymptotic analysis,where z represents the propagation variable or spatial coordinate.We also graphically observe that:the velocities of the first-and second-order breathers will be affected by h1(z),p1(z),r1(z),and s1(z),and the background wave depends on g(z).

Optimization of inter-seasonal nitrogen allocation increases yield and resource-use efficiency in a water-limited wheat-maize cropping system in the North China Plain

Winter wheat–summer maize cropping system in the North China Plain often experiences droughtinduced yield reduction in the wheat season and rainwater and nitrogen(N)fertilizer losses in the maize season.This study aimed to identify an optimal interseasonal water-and N-management strategy to alleviate these losses.Four ratios of allocation of 360 kg N ha^(-1)between the wheat and maize seasons under one-time presowing root-zone irrigation(W0)and additional jointing and anthesis irrigation(W2)in wheat and one irrigation after maize sowing were set as follows:N1(120:240),N2(180:180),N3(240:120)and N4(300:60).The results showed that under W0,the N3 treatment produced the highest annual yield,crop water productivity(WPC),and nitrogen partial factor productivity(PFPN).Increased N allocation in wheat under W0 improved wheat yield without affecting maize yield,as surplus nitrate after wheat harvest was retained in the topsoil layers and available for the subsequent maize.Under W2,annual yield was largest in the N2 treatment.The risk of nitrate leaching increased in W2 when N application rate in wheat exceeded that of the N2 treatment,especially in the wet year.Compared to W2N2,the W0N3 maintained 95.2%grain yield over two years.The WPCwas higher in the W0 treatment than in the W2 treatment.Therefore,following limited total N rate,an appropriate fertilizer N transfer from maize to wheat season had the potential of a“triple win”for high annual yield,WPCand PFPN in a water-limited wheat–maize cropping system.

Research on cloud computing and services framework of marine environmental information management

Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.

Automatic Quality Inspection of Bakery Products Based on Shape and Color Information

An automatic intelligent system for the colour and texture inspection of bakery products is proposed.In this system,advance classification technique featuring Support Vector Machine and biologically inspired HMAX based shape descriptor integrated with biologically plausible RGB Opponent-Colour-Channel Descriptor is used to classify bakery products to their respective classes based on the shape and based on their colour referring to different baking durations. The results of this paper are compared with other methods for the automatic bakery products inspection. It is discovered that biologically inspired computer vision models performs accurately and efficiently as compared to the computer vision models which are not biologically plausible,in the bakery products quality inspection. It is also discovered that the One Versus One SVM and Directed Acyclic Graph SVM acquired the maximum accurate classification rate. The proposed method acquired classification accuracy of 95% and 100% for the biscuit shape and biscuit colour recognition,respectively. The proposed method is also consistently stable and invariant. This shows that the biologically inspired computer vision models have the capability to replace existing inspection methods as more reliable and accurate alternative.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Information-Theoretic Limits on Compression of Semantic Information

As conventional communication systems based on classic information theory have closely approached Shannon capacity,semantic communication is emerging as a key enabling technology for the further improvement of communication performance.However,it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission.In this paper,we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network.We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence.We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function.We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder,and obtain the corresponding rate distortion function.We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.

A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting fromcyber-attacks.Firstly,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data restoration.Secondly,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on HF.Finally,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available capacity.Experiments show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

The Combined Effect of Spin-Transfer Torque and Voltage-Controlled Strain Gradient on Magnetic Domain-Wall Dynamics:Toward Tunable Spintronic Neuron

Magnetic domain wall(DW), as one of the promising information carriers in spintronic devices, have been widely investigated owing to its nonlinear dynamics and tunable properties. Here, we theoretically and numerically demonstrate the DW dynamics driven by the synergistic interaction between current-induced spin-transfer torque(STT) and voltage-controlled strain gradient(VCSG) in multiferroic heterostructures. Through electromechanical and micromagnetic simulations, we show that a desirable strain gradient can be created and it further modulates the equilibrium position and velocity of the current-driven DW motion. Meanwhile, an analytical Thiele's model is developed to describe the steady motion of DW and the analytical results are quite consistent with the simulation data. Finally, we find that this combination effect can be leveraged to design DW-based biological neurons where the synergistic interaction between STT and VCSG-driven DW motion as integrating and leaking motivates mimicking leaky-integrate-and-fire(LIF) and self-reset function. Importantly, the firing response of the LIF neuron can be efficiently modulated, facilitating the exploration of tunable activation function generators, which can further help improve the computational capability of the neuromorphic system.

Coordinated voltage regulation strategy of OLTC and BESS considering switching delay

When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap changer(OLTC)can adjust the transformer winding tap to maintain the secondary side voltage within the normal range.However,the inevitable delay in switching transformer taps makes it difficult to respond quickly to voltage fluctuations.Moreover,switching the transformer taps frequently will decrease the service life of OLTC.In order to solve this critical issue,a cooperative voltage regulation strategy applied between the battery energy storage systems(BESSs)and OLTSs.is proposed By adjusting the charge and discharge power of BESSs,the OLTC can frequently switch the transformer taps to achieve rapid voltage regulation.The effectiveness of the proposed coordinated regulation strategy is verified in the IEEE 33 node distribution systems.The simulation results show that the proposed coordinated regulation strategy can stabilize the voltage of the distribution network within a normal range and reduce the frequency of tap switching,as such elongating the service life of the equipment.

Water inrush evaluation of coal seam floor by integrating the water inrush coefficient and the information of water abundance

The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.

Collection System of Soil Moisture Information Based on ZigBee and GPRS

Real-time monitoring and wireless transmission of farmland soil moisture have been paid with more and more attention in the research of agricultural drought monitoring, early warning and prevention and control technology. The hardware design and software design of soil moisture monitoring in farmland were carried out, and a monitoring system based on the principles of ZigBee and GPRS technologies was developed and applied to the actual monitoring of soil moisture in farmland. This study provides a good idea to promote real-time monitoring, wireless transmission and intelligent management of soil moisture in farmland.

Borrower’s default and self-disclosure of social media information in P2P lending

Background:We examine the signaling effect of borrowers’social media behavior,especially self-disclosure behavior,on the default probability of money borrowers on a peer-to-peer(P2P)lending site.Method:We use a unique dataset that combines loan data from a large P2P lending site with the borrower’s social media presence data from a popular social media site.Results:Through a natural experiment enabled by an instrument variable,we identify two forms of social media information that act as signals of borrowers’creditworthiness:(1)borrowers’choice to self-disclose their social media account to the P2P lending site,and(2)borrowers’social media behavior,such as their social network scope and social media engagement.Conclusion:This study offers new insights for screening borrowers in P2P lending and a novel usage of social media information.

Quantification of Dance Movement by Simultaneous Measurement of Body Motion and Biophysical Information

The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical information. In this research, two kinds of same leg movement are captured by simultaneous measurement; one is a leg movement with given strength, the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography （EMG） equipment in order to quantitatively analyze characteristics of leg movement. Also, we measured the motion of the traditional Japanese dance using the constructed system. We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data. In addition, we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.