SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

An Information Hiding Algorithm Based on Bitmap Resource of Portable Executable File

An information hiding algorithm is proposed, which hides information by embedding secret data into the palette of bitmap resources of portable executable （PE） files. This algorithm has higher security than some traditional ones because of integrating secret data and bitmap resources together. Through analyzing the principle of bitmap resources parsing in an operating system and the layer of resource data in PE files, a safe and useful solution is presented to solve two problems that bitmap resources are incorrectly analyzed and other resources data are confused in the process of data embedding. The feasibility and effectiveness of the proposed algorithm are confirmed through computer experiments.

A hybrid neural network system for prediction and recognition of promoter regions in human genome

This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm.Evaluation on Human chromosome 22 was ～66% in sensitivity and ～48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

3D Modeling and Simulation of Fancy Fabrics in Weft Knitting

Weft knitted fancy fabrics are widely used in knitted garment design. Due to the complexity of the structures, their modeling and simulation needs to be solved in three-dimensional (3D) CAD developments. In this paper, 3D loop geometrical models of weft knitted fancy structures, including tuck stitch, jacquard stitch, transfer stitch and fleecy stitch, were developed based on an improved model of plain loop, and their central axes as some 3D space curves were achieved by using Non-Uniform Rational B-Splines (NURBS). The 3D visual simulation programme was written in C++ programming language using OpenGL, which was a function library of 3D graphics. Some examples of weft knitted fancy fabrics were generated and practical application of 3D simulation was discussed.

H∞ synchronization of the coronary artery system with input time-varying delay

This paper investigates the H_∞ synchronization of the coronary artery system with input delay and disturbance.We focus on reducing the conservatism of existing synchronization strategies.Base on the triple integral forms of the Lyapunov–Krasovskii functional（LKF）,we utilize single and double integral forms of Wirtinger-based inequality to guarantee that the synchronization feedback controller has good performance against time-varying delay and external disturbance.The effectiveness of our strategy can be exhibited by simulations under the different time-varying delays and different disturbances.

Robust H_∞ control of piecewise-linear chaotic systems with random data loss

This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data loss occurs intermittently, which appears typically in a network environment）. The data loss is modelled as a random process which obeys a Bernoulli distribution. In the face of random data loss, a piecewise controller is designed to robustly stabilize the networked system in the sense of mean square and also achieve a prescribed H∞ disturbance attenuation performance based on a piecewise-quadratic Lyapunov function. The required H∞ controllers can be designed by solving a set of linear matrix inequalities （LMIs）. Chua＇s system is provided to illustrate the usefulness and applicability of the developed theoretical results.

Design and Implementation of Memory Access Fast Switching Structure in Cluster-Based Reconfigurable Array Processor

Memory access fast switching structures in cluster are studied,and three kinds of fast switching structures（ FS,LR2 SS,and LAPS） are proposed. A mixed simulation test bench is constructed and used for statistic of data access delay among these three structures in various cases. Finally these structures are realized on Xilinx FPGA development board and DCT,FFT,SAD,IME,FME,and de-blocking filtering algorithms are mapped onto the structures. Compared with available architectures,our proposed structures have lower data access delay and lower area.

On the Set of 2 - Common Consequent of Primitive Digraphs with Exact <i>d</i>Vertices Having Loop

Let d and n are positive integers, n≥2,1≤d≤ 2.In this paper we obtain that the set of the 2 - common consequent of primitive digraphs of order n with exact d vertices having loop is{1,2,…, n-[]}.

A component-based back-propagation reliability model with low complexity for complex software systems

Since most of the available component-based software reliability models consume high computational cost and suffer from the evaluating complexity for the software system with complex structures,a component-based back-propagation reliability model(CBPRM)with low complexity for the complex software system reliability evaluation is presented in this paper.The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses.These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation.CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models.Another advantage of CBPRM over others is its robustness.CBPRM depends on the component reliabilities and the correlative sensitivities,which are independent from the software system structure.Based on the theory analysis and experiment results,it shows that the complexity of CBPRM is evidently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.

The Impact of E-commerce on the Development of Regional Economy and Its Analysis of Security Strategy

The rise of electronic commerce has broken the traditional trading mode, changed people＇ s production and life, has gradually become an important factor of regional economic development. The paper comprehensively discuss on present situation and characteristics of electronic commerce development in our country, on basis of it, the paper analyze electronic commerce major impact on regional economic development, and put forward to strengthen the development of e-commerce related strategies to promote regional economic development role.

Architecture of Integrated Data Clustering Machine

Data clustering is a significant information retrieval technique in today＇s data intensive society. Over the last few decades a vast variety of huge number of data clustering algorithms have been designed and implemented for all most all data types. The quality of results of cluster analysis mainly depends on the clustering algorithm used in the analysis. Architecture of a versatile, less user dependent, dynamic and scalable data clustering machine is presented. The machine selects for analysis, the best available data clustering algorithm on the basis of the credentials of the data and previously used domain knowledge. The domain knowledge is updated on completion of each session of data analysis.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Protecting LLMs against Privacy Attacks While Preserving Utility

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. This inadvertent leakage of sensitive information typically occurs when the models are subjected to black-box attacks. To address the growing concerns of safeguarding private and sensitive information while simultaneously preserving its utility, we analyze the performance of Targeted Catastrophic Forgetting (TCF). TCF involves preserving targeted pieces of sensitive information within datasets through an iterative pipeline which significantly reduces the likelihood of such information being leaked or reproduced by the model during black-box attacks, such as the autocompletion attack in our case. The experiments conducted using TCF evidently demonstrate its capability to reduce the extraction of PII while still preserving the context and utility of the target application.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village

Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that would identify and solve the problem. At present, we live in a world dominated by technology on a significant scale, major network coverage, high-end smart-phones, as long as there are great discoveries and improvements in AI. The combination of high-end smart-phones and computer vision via Deep Learning has made possible what can be defined as “smartphone-assisted disease diagnosis”. In the area of Deep Learning, multiple architecture models have been trained, some achieving performance reaching more than 99.53% [1]. In this study, we evaluate CNN’s architectures applying transfer learning and deep feature extraction. All the features obtained will also be classified by SVM and KNN. Our work is feasible by the use of the open source Plant Village Dataset. The result obtained shows that SVM is the best classifier for leaf’s diseases detection.

Low-complexity PTS scheme based on phase factor sequences optimization

In this paper, a new partial transmit sequence（PTS）scheme with low computational complexity is proposed for the problems of high computational complexity in the conventional PTS method. By analyzing the relationship of candidate sequences in the PTS method under the interleaved partition method, it has been discovered that some candidate sequences generated by phase factor sequences have the same peak average power ratio（PAPR）. Hence, phase factor sequences can be optimized to reduce their searching times. Then, the computational process of generating candidate sequences can be simplified by improving the utilization of data and minimizing the calculations of complex multiplication. The performance analysis shows that, compared with the conventional PTS scheme, the proposed approach significantly decreases the computational complexity and has no loss of PAPR performance.

Geographic,Geometrical and Semantic Reconstruction of Urban Scene from High Resolution Oblique Aerial Images

An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage,an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery.Finally the point cloud is classified into three classes(building,vegetation, and ground) by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently.By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from86.75% to 93.02% are obtained.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

Methylation profile of bovine Oct4 gene coding region in relation to three germ layers

Previous studies have shown that octamer-binding transcription factor 4（Oct4） plays a significant role in early embryonic development of mammalian animals, and different Oct4 expression levels induce multi-lineage differentiation which are regulated by DNA methylation. To explore the relationship between the methylation pattern of Oct4 gene exon 1 and embryonic development, in this work, five different tissues（heart, liver, lung, cerebrum and cerebellum） from three germ layers were chosen from low age（50–60 d） and advanced age（60–70 d） of fetal cattle and the differences between tissues or ages were analyzed, respectively. The result showed that the DNA methylation level of Oct4 gene exon 1 was significant different（P〈0.01） between any two of three germ layers in low age（〈60 d）, but kept steady of advanced age（P〉0.05）（〉60 d）, suggesting that 60-d post coital was an important boundary for embryonic development. In addition, in ectoderm（cerebrum and cerebellum）, there was no significant methylation difference of Oct4 gene exon 1 between low age and advanced age（P〉0.05）, but the result of endoderm（liver and lung） and mesoderm（heart） were on the contrary（P〈0.01）, which indicated the development of ectoderm was earlier than endoderm and mesoderm. The methylation differences from the 3rd, 5th and 9th Cp G-dinucleotide loci of Oct4 gene exon 1 were significantly different between each two of three germ layers（P〈0.05）, indicating that these three loci may have important influence on bovine embryonic development. This study showed that bovine germ layers differentiation was significantly related to the DNA methylation status of Oct4 gene exon 1. This work firstly identified the DNA methylation profile of bovine Oct4 gene exon 1 and its association with germ layers development in fetus and adult of cattle. Moreover, the work also provided epigenetic information for further studying bovine embryonic development and cellular reprogramming.