Picture模糊数的改进得分函数在多属性群决策中的应用

针对picture模糊环境下专家权重和属性权重信息未知的多属性群决策问题进行研究.首先,针对现有picture模糊数的得分函数存在一些不足问题,提出了一种新的得分函数.通过分析其相关性质,并与现有得分函数进行比较,说明其更具合理性,能更好地处理模糊决策信息.其次,提出了基于得分函数的picture模糊环境下的多属性群决策方法,根据每位专家给出的picture模糊评价信息的不精确程度及不同专家给出信息的差异程度,从个体和群体两方面综合得到决策者权重;利用得分函数构造不同属性的灰靶贡献度,从而确定属性权重.最后,通过实例比较分析验证了所提方法的可行性和有效性.

Fast mode decision for Hierarchical-B pictures in scalable video coding

To decrease the computational complexity of adaptive inter-layer prediction and improve the encoding efficiency in sealable video coding, a mode decision algorithm is proposed by exploiting the part of used candidate modes of the co-located reference macrobloeks for Hierarchical-B pictures. This scheme reduces the amount of the candidate modes to generate a dynamic list for the current encoding macroblock according to the statistical information derived from the co-located reference macroblocks in different temporal levels. The experimental results show that this fast algorithm reduces approximately 31% encoding time on average with the negligible loss of encoding performance.

Two Comments on Predictive Picture Coding

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Error Resilient Video Communication Based on Picture Level Adaptive Frame / Field Coding

Packet loss protection method based on picture level adaptive frame /field coding (PAFF)was presented. Firstly,the end-to-end rate-distortion analysis for PAFF on the current frame was performed. Secondly,in order to evaluate the severity of error propagation in the following frames,the error propagation intensity and human visual quality sensitivity of different areas were taken into consideration. It was followed by the quantification of relative importance. Finally,the proper coding mode was chosen utilizing an unequal comparison procedure. The simulation results show that the proposed method can improve peak signal-to-noise ratio (PSNR) up to 0. 9 dB and 1. 6 dB comparing with the field only and the dispersed flexible macro-block ordering (FMO)only methods respectively.

Picture最小二乘模糊推理算法在模式识别中的应用

目的:由于picture模糊推理全蕴涵算法和picture模糊推理五蕴涵算法所满足的还原性是有条件的还原性,我们寻找一种满足无条件还原性的picture模糊推理算法。方法:将picture模糊集转化为三维模糊集,结合最小二乘模型的思想,构建最小二乘模型的picture模糊推理算法。结果:给出基于最小二乘模型的picture模糊推理算法及其解的表示形式。结论:我们将基于最小二乘模型的picture模糊推理算法应用到模式识别,实验结果表明,本文提出的算法是合理和有效的。

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

The Application of Nicotiana benthamiana as a Transient Expression Host to Clone the Coding Sequences of Plant Genes

Coding sequences (CDS) are commonly used for transient gene expression, in yeast two-hybrid screening, to verify protein interactions and in prokaryotic gene expression studies. CDS are most commonly obtained using complementary DNA (cDNA) derived from messenger RNA (mRNA) extracted from plant tissues and generated by reverse transcription. However, some CDS are difficult to acquire through this process as they are expressed at extremely low levels or have specific spatial and/or temporal expression patterns in vivo. These challenges require the development of alternative CDS cloning technologies. In this study, we found that the genomic intron-containing gene coding sequences (gDNA) from Arabidopsis thaliana, Oryza sativa, Brassica napus, and Glycine max can be correctly transcribed and spliced into mRNA in Nicotiana benthamiana. In contrast, gDNAs from Triticum aestivum and Sorghum bicolor did not function correctly. In transient expression experiments, the target DNA sequence is driven by a constitutive promoter. Theoretically, a sufficient amount of mRNA can be extracted from the N. benthamiana leaves, making it conducive to the cloning of CDS target genes. Our data demonstrate that N. benthamiana can be used as an effective host for the cloning CDS of plant genes.

A quantum blind signature scheme based on dense coding for non-entangled states

In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.

An Integrated Bipolar Picture Fuzzy Decision Driven System to Scrutinize Food Waste Treatment Technology through Assorted Factor Analysis

Food Waste(FW)is a pressing environmental concern that affects every country globally.About one-third of the food that is produced ends up as waste,contributing to the carbon footprint.Hence,the FW must be properly treated to reduce environmental pollution.This study evaluates a few available Food Waste Treatment(FWT)technologies,such as anaerobic digestion,composting,landfill,and incineration,which are widely used.A Bipolar Picture Fuzzy Set(BPFS)is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model.A novel Criteria Importance Through Intercriteria Correlation-Stable Preference Ordering Towards Ideal Solution(CRITIC-SPOTIS)approach is developed to objectively analyze FWT selection based on thirteen criteria covering the industry’s technical,environmental,and entrepreneurial aspects.The CRITIC method is used for the objective analysis of the importance of each criterion in FWT selection.The SPOTIS method is adopted to rank the alternative hassle-free,following the criteria.The proposed model offers a rank reversal-free model,i.e.,the rank of the alternatives remains unaffected even after the addition or removal of an alternative.In addition,comparative and sensitivity analyses are performed to ensure the reliability and robustness of the proposed model and to validate the proposed result.

NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

Single Photon Detection Technology in Underwater Wireless Optical Communication:Modulation Modes and Error Correction Coding Analysis

This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication performance.The study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization multiplexing.Furthermore,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding types.The effects of unscattered photon ratio and depolarization ratio on BER are also verified.Finally,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.

Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.

顺应论视域下The Picture of Dorian Gray中译本的对比研究

The Picture of Dorian Gray是王尔德唯一一部长篇小说,小说语言唯美自然。本文从顺应论的角度出发,从语境顺应、结构顺应以及动态顺应三个方面分析孙宜学译本与荣如德译本的差异。文章发现,两译者对于自己的定位不同,即前者是源语文化的传播者,后者则是文学作品的传播者,译者的意识程度以及自身定位影响着译者采取的翻译方法,只有找准定位才能更好地翻译,获得更大的阅读价值以及参考价值。

A Study on the Values and Strategies of English Picture Book Reading for Improving Pupils’ English Reading Literacy

Reading is one of the core skills in English learning.It is indispensable for EFL learners to develop good English reading literacy in primary schools.English picture books are consistent with pupils’cognitive and thinking modes,which is valuable to improve their English reading literacy.However,at present,most Chinese pupils have some problems in English reading.The paper aims at analyzing the current problems in Chinese pupils’English reading and digging out the values and strategies of English picture book reading,so as to stimulate their reading interests,cultivate their intercultural awareness,and improve their English reading literacy.

Adoption of network and plan-do-check-action in the international classification of disease 10 coding

BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also achieved favorable results in clinical medical record management.However,research on their combined application is relatively lacking.Objective:it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding.Material and Method:a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.AIM To study the adoption of network and PDCA in the ICD-10.METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.RESULTS In the 3,6,12,18,and 24 months of PDCA cycle management mode,the coding accuracy and medical record completion rate were higher,and the coding time was lower in the observation group as against the controls(P<0.05).The satisfaction of coders(80.22%vs 53.45%)and patients(84.89%vs 51.72%)in the observation group was markedly higher as against the controls(P<0.05).CONCLUSION The combination of computer networks and PDCA can improve the accuracy,efficiency,completion rate,and satisfaction of ICD-10 coding.

Adaptive Hybrid Forward Error Correction Coding Scheme for Video Transmission

This paper proposes an adaptive hybrid forward error correction(AH-FEC)coding scheme for coping with dynamic packet loss events in video and audio transmission.Specifically,the proposed scheme consists of a hybrid Reed-Solomon and low-density parity-check(RS-LDPC)coding system,combined with a Kalman filter-based adaptive algorithm.The hybrid RS-LDPC coding accommodates a wide range of code length requirements,employing RS coding for short codes and LDPC coding for medium-long codes.We delimit the short and medium-length codes by coding performance so that both codes remain in the optimal region.Additionally,a Kalman filter-based adaptive algorithm has been developed to handle dynamic alterations in a packet loss rate.The Kalman filter estimates packet loss rate utilizing observation data and system models,and then we establish the redundancy decision module through receiver feedback.As a result,the lost packets can be perfectly recovered by the receiver based on the redundant packets.Experimental results show that the proposed method enhances the decoding performance significantly under the same redundancy and channel packet loss.

Recent Advances in Video Coding for Machines Standard and Technologies

To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization procedures.In this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are given.Then the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM conference.Finally,we give conclusions.

基于FANUC PICTURE工具的FANUC数控系统HMI二次开发

FANUC PICTURE具有较强的图形显示功能,具有上手简单,开发周期短等特点,是使用最为广泛的FANUC数控系统HMI二次开发工具。本文对该开发工具进行了解析,并通过项目实例完整地展示了使用该工具进行二次开发HMI的操作。