Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

家庭云NAS研究及推广策略

随着智能家居、高清视频以及全民泛资讯新媒体业务的发展,个人与家庭所需的存储容量急剧增长。传统的家庭存储面临价格昂贵、使用复杂、安全无保障以及访问速度慢等缺点,无法满足用户需求。利用庞大的千兆家庭宽带用户基础和云计算的技术优势,运营商具备切入家庭存储市场的便利条件。文章详细分析了家庭云存储的特点、场景及技术架构,针对用户痛点分析如何推广家庭云产品。

Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Performance analysis of an iSCSI-based unified storage network

In this paper, we introduced a novel storage architecture 'Unified Storage Network', which merges NAC( Network Attached Channel) and SAN( Storage Area Network) , and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN( Unified Storage Network) . To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.

Spinnaker Networks SpinServer 3300——管理NAS与不同品牌的存储设备

Spinnaker Networks的Spin Server3300是一部具备高可伸缩性的NAS服务器产品，它可以组合并管理第三方的光纤通道RAID存储产品．以及支持通用的协议，如通用Internet文件系统(Common Internet File System，简称CIFS)和UNIX NFS等。这个系统包含两个核心部件SPinServer

Research on the Security of the United Storage Network Based on NAS

A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.

Research on Distribution Network Automation and Distribution Network Planning Mode

Based on the research of distribution network automation and distribution network planning mode, the analysis of the significance of urban distribution network automation must be performed at the first place. Combined with the problems existing in China’s current distribution network, it is concluded that, establish effective hardware support system, data sharing and feeder automation to ensure automation safety;strengthen power distribution and power line material testing to improve distribution automation system and distribution network planning;research methods of improving the professional skills and comprehensive quality of professionals.

NASSN:a NAS-based storage network

With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve the above-mentioned issues,a NAS-based storage network(for short NASSN)has been designed.Firstly,the NASSN integrates multi-NAS,iNAS(an iSCSI-based NAS)and enterprise SAN with the help of storage virtualization,which can provide a greater capacity and better scalability.Secondly,the NASSN can provide high availability with the help of server and storage subsystem redundancy technologies.Thirdly,the NASSN simultaneously serves for both the file I/O and the block I/O with the help of an iSCSI module,which has the advantages of NAS and SAN.Finally,the NASSN can provide higher I/O speed by a high network-attached channel which implements the direct data transfer between the storage device and client.In the experiments,the NASSN has ultra-high-throughput for both of the file I/O requests and the block I/O requests.

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

The Impact of Internet of Things in Manufacturing Management

The study investigated the impact of the Internet of Things in manufacturing management. Specifically, the study examined how IoT implementation and management affect organizational efficiency in Camanov Ltd.;and to what extent IoT implementation contributes to the saving of cost and time of the organization. The research design is a survey. The population of this study consisted of all 141 staff of Camanov Ltd. Port Harcourt. Since the population is not large, the researcher conducted a census of all, and 126 staff completed a structured questionnaire. The two research questions were analyzed using simple percentages and all two hypotheses were tested using sample proportion statistics (Z test) at a 0.05 level of significance. The result showed that the Internet of Things has a significant impact on organizational efficiency in Camanov Ltd. (Z = 4.73);and that the Internet of Things significantly contributes toward saving cost and time of the organization Camanov Ltd (Z = 4.95). It was recommended that organizations should encourage training of personnel in the improved limitless possibility of information gathered from the Internet of Things framework which supports planning, budgeting and monitoring approaches, providing more reliable information to support actions, in particular in the decision-making process, to enhance productivity.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

神经架构搜索综述

近几年,深度学习因具有强大的表征能力,已经在许多领域中取得了突破性的进展,而神经网络的架构对它的性能至关重要。然而,高性能的神经网络架构设计严重依赖研究人员的先验知识和经验,神经网络参数量庞大,难以设计最优的神经网络架构,因此自动神经架构搜索(NAS)获得了极大的关注。NAS是一种使用机器学习的方法,可以在不需要大量人力的情况下,自动搜索最优网络架构的技术,是未来神经网络设计的重要手段之一。NAS本质上是一个搜索优化问题,通过对搜索空间、搜索策略和性能评估策略的设计,自动搜索最优的网络结构。从搜索空间、搜索策略和性能评估策略这3个方面详细且全面地分析、比较和总结目前NAS的研究进展,方便读者快速了解神经架构搜索的发展过程和各项技术的优缺点,并提出NAS未来可能的研究发展方向。

基于NAS技术的风电场数据备份实施案例

响应国家新能源发展战略,风电场相较于前几年,在信息化发展及网络信息安全方面有较大提高。文章实施案例来源于一线长期实际部署,真实风电场环境下实施,方案真实性、可操作性及应用性也已经得到各方的认可。方案的主要目的是防止服务器在出现灾难性故障的情况下,风电场运行数据丢失,数据无备份恢复,提出的基于NAS技术的NAS存储服务器组建的局域网平台,搭建自动化数据备份系统方案,实现对风电场中央控制监控系统的全局备份,为灾难性故障后的数据恢复提供可靠支撑。

基于附网数据存储技术(NAS)实现企业数据高效安全存储应用的研究

近年来,企业数据的规模和重要性不断增加,如何有效且安全地存储和管理这些数据成为企业面临的重要挑战。附网数据存储技术(NAS)作为一种专门用于数据存储和文件共享的解决方案,为企业提供了高效、安全的数据存储和管理方式。本文通过一家企业的案例研究,探讨了附网数据存储技术在实现企业数据高效、安全存储方面的应用情况。