Design and high-power testing of offline conditioning cavity for CiADS RFQ high-power coupler

To validate the design rationality of the power coupler for the RFQ cavity and minimize cavity contamination,we designed a low-loss offline conditioning cavity and conducted high-power testing.This offline cavity features two coupling ports and two tuners,operating at a frequency of 162.5 MHz with a tuning range of 3.2 MHz.Adjusting the installation angle of the coupling ring and the insertion depth of the tuner helps minimize cavity losses.We performed electromagnetic structural and multiphysics simulations,revealing a minimal theoretical power loss of 4.3%.However,when the cavity frequency varied by110 kHz,theoretical power losses increased to10%,necessitating constant tuner adjustments during conditioning.Multiphysics simulations indicated that increased cavity temperature did not affect frequency variation.Upon completion of the offline high-power conditioning platform,we measured the transmission performance,revealing a power loss of 6.3%,exceeding the theoretical calculation.Conditioning utilized efficient automatic range scanning and standing wave resonant methods.To fully condition the power coupler,a 15°phase difference between two standing wave points in the condition-ing system was necessary.Notably,the maximum continuous wave power surpassed 20 kW,exceeding the expected target.

Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making

Reinforcement Learning(RL)has emerged as a promising data-driven solution for wargaming decision-making.However,two domain challenges still exist:(1)dealing with discrete-continuous hybrid wargaming control and(2)accelerating RL deployment with rich offline data.Existing RL methods fail to handle these two issues simultaneously,thereby we propose a novel offline RL method targeting hybrid action space.A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent way.This allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid policy.Critically,a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution actions.Our method demonstrates superior performance and generality across different tasks,particularly in typical realistic wargaming scenarios.

An Investigation on the Application of Online and Offline Hybrid Teaching in Local Anatomy Course of Acupuncture and Moxibustion Specialty

The purpose of this paper is to explore the application of online and offline blended teaching in local anatomy courses of acupuncture specialty.It introduces the concept and characteristics of blended teaching mode and analyzes the respective advantages of online and offline teaching as well as the advantages and application prospects of blended teaching mode.The teaching status quo of local anatomy courses in acupuncture and moxibustion is analyzed,pointing out the problems of the traditional teaching mode and the application prospect of the blended teaching mode.In the practical part,the preparation and design of online teaching resources,the design of offline practical teaching sessions,and the evaluation methods of teaching effect are introduced in detail.This study aims to provide new teaching modes and ideas for teaching acupuncture and moxibustion and promote the improvement of teaching quality.

Research on the Blended Online and Offline Teaching Mode of Ideological and Political Courses in Universities

With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.

Construction Ideas of Musculoskeletal Rehabilitation First-class Undergraduate Course Based on Online and Offline Blended Teaching

In order to combine the advantages of online teaching and traditional offline classroom teaching,this paper optimizes the teaching design by taking Musculoskeletal Rehabilitation for undergraduates as the carrier,and reconstructs the course according to five parts:basic theory course,practical training course,standardized patient,case report,and course evaluation.Through analyzing the classroom quality and teaching effect,the innovation and practical effect of course reconstruction are explored.With students as the main body and goals as the guide,this model gives full play to the initiative and creativity of students,meets the individual needs of students at different levels,and provides reference ideas for improving the advanced,innovative and challenging creation of the course.

Online and Offline Integrated Teaching of Histology and Embryology Based on Concept of "Golden Course"

Taking construction of the online and offline integrated first-class undergraduate curriculum teaching modes of Histology and Embryology in Guangxi as an opportunity,under the guidance of student-centered teaching concept,efforts were made to innovate online and offline integrated teaching mode to overcome the shortcomings and dilemma of traditional Histology and Embryology teaching,with attention paid to the competence education in students including schematic knowledge,professional techniques,analytical thinking,and ideological and political theories,which would be of great significance for the cultivation of high-quality professionals specialized in traditional Chinese medicine.

Boundary Data Augmentation for Offline Reinforcement Learning

Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online interaction.One of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch between the knowledge of the learned policy and the reality of the underlying environment.Recent works usually handle this in a too pessimistic manner to avoid out-of-distribution(OOD)queries as much as possible,but this can influence the robustness of the agents at unseen states.In this paper,we propose a simple but effective method to address this issue.The key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions,and we propose to find those regions by simulating them with modified Generative Adversarial Nets(GAN)such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by themselves,with regard to the behavior policy or some other reference policy.We then use this information to regularize the ORL algorithm to penalize the overconfidence behavior in these regions.Extensive experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.

On the “Online and Offline Blended Teaching” Mode for Ideological and Political Theory Courses in Universities—A Case of “Introduction to Basic Principles of Marxism”

With the deepening development of educational informatization, online and offline blended teaching, as a new teaching mode, is increasingly receiving widespread attention from educators [1]. At present, the reform of the “online and offline blended teaching” of ideological and political education courses directly affects the quality of talent cultivation in universities. The article takes the course “Introduction to Basic Principles of Marxism” as an example to explore the reform mode of “online and offline blended teaching” in ideological and political theory courses in universities from the aspects of reasonable allocation of class hours, design of online teaching activities, how to deepen classroom teaching offline, and diversified assessment modes. Furthermore, the article summarizes the experience of mode reform and promotes the deep development of the people-oriented education concept in ideological and political courses in universities, so as to achieve the ultimate goal of moral education in ideological and political education in universities.

10层陆面过程模式及其Offline独立试验

利用西伯利亚地区的一个试验点资料和1998年中国淮河流域试验(HUBEX)的加密观测资料,对一个新发展的陆面过程模式进行了模拟检验。西伯利亚地区的单点试验表明,不同时间间隔的边界强迫对地表吸收的净短波辐射和释放的潜热影响较大。淮河流域的模拟结果表明,模式能够较好地模拟出我国夏季半湿润地区陆面特征量的变化趋势。由于模式模拟的地温偏低、净短波辐射偏小,所以模拟的感热和潜热值偏小。对该模式在淮河流域的植被、土壤等参数的合理选取可能会提高模式的模拟效果。

基于Online to Offline的高职航海技术专业现代学徒制教学生态构建

以高职航海技术专业为例,分析现代学徒制培养模式下的瓶颈问题,探究引入O2O(Online to Offline)教学方式,在线上线下形成学生、学校和企业共同参与的工学结合、产教融合的教学生态,提高航海技术专业学徒制学生的自主学习能力、专业技能和综合素质,培养满足社会和航运企业需求的航海人才。

在线课程背景下高职英语online+offline教学模式的应用研究

通过对当前英语教学中存在问题的统计分析,依托在线开放课程平台,利用英语课前、课上和课后教学实践活动验证英语online+offline教学新模式下的教学设计、教学组织形式、教学方法手段、考核评价方式等对英语教学效果的提升作用。

基于在线开放课程平台的高职英语“online+offline”学习方式研究

随着教育信息化理念指导各学科教学的不断深入,有关信息化课堂教学中学生学习方式的研究成为不可忽视的关注点。基于在线开放课程信息化平台对英语教学及学习的支持,以建构主义学习理论为指导,探讨现代信息环境下英语主要教学环节与学习方式的关系,阐释通过自主学习、合作学习等方式在英语"online+offline"课堂教学中有效开展学习活动,提升学生学习效率,为英语课堂教学改革提供参考。

Offline two-dimensional liquid chromatography coupled with ion mobility-quadrupole time-of-flight mass spectrometry enabling fourdimensional separation and characterization of the multicomponents from white ginseng and red ginseng

Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification.A dimension-enhanced strategy,by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry(2 D-LC/IM-QTOF-MS)enabling four-dimensional separations(2 D-LC,IM,and MS),is proposed.In combination with in-house database-driven automated peak annotation,this strategy was utilized to characterize ginsenosides simultaneously from white ginseng(WG)and red ginseng(RG).An offline 2 DLC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides.Ginsenoside analysis was performed by data-independent high-definition MSE(HDMSE)in the negative ESI mode on a Vion?IMS-QTOF hybrid high-resolution mass spectrometer,which could better resolve ginsenosides than MSEand directly give the CCS information.An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds,was established to assist the identification of ginsenosides.Streamlined workflows,by applying UNIFI?to automatedly annotate the HDMSEdata,were proposed.We could separate and characterize 323 ginsenosides(including 286 from WG and 306 from RG),and 125 thereof may have not been isolated from the Panax genus.The established 2 D-LC/IM-QTOF-HDMSEapproach could also act as a magnifier to probe differentiated components between WG and RG.Compared with conventional approaches,this dimensionenhanced strategy could better resolve coeluting herbal components and more efficiently,more reliably identify the multicomponents,which,we believe,offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.

Improved Generalized Predictive Control Algorithm with Offline and Online Identification and Its Application to Fixed Bed Reactor

An improved generalized predictive control algorithm is presented in thispaper by incorporating offline identification into online identification. Unlike the existinggeneralized predictive control algorithms, the proposed approach divides parameters of a predictivemodel into the time invariant and time-varying ones, which are treated respectively by offline andonline identification algorithms. Therefore, both the reliability and accuracy of the predictivemodel are improved. Two simulation examples of control of a fixed bed reactor show that this newalgorithm is not only reliable and stable in the case of uncertainties and abnormal disturbances,but also adaptable to slow time varying processes.

A Model for Regional Energy Utilization by Offline Heat Transport System and Distributed Energy Systems—Case Study in a Smart Community, Japan

Under the Kyoto Protocol,Japanwas supposed to reduce six percent of the green house gas (GHG) emission in 2012. However, until the year 2010, the statistics suggested that the GHG emission increased 4.2%. What is more challenge is, afterFukushimacrisis, without the nuclear energy,Japanmay produce about 15 percent more GHG emissions than1990 inthis fiscal year. It still has to struggle to meet the target set by Kyoto Protocol. The demonstration area of “smart community” suggests Japanese exploration for new low carbon strategies. The study proposed a demand side response energy system, a dynamic tree-like hierarchical model for smart community. The model not only conveyed the concept of smart grid, but also built up a smart heat energy supply chain by offline heat transport system. Further, this model promoted a collaborative energy utilization mode between the industrial sector and the civil sector. In addition, the research chose the smart community inKitakyushuas case study and executed the model. The simulation and the analysis of the model not only evaluate the environmental effect of different technologies but also suggest that the smart community inJapanhas the potential but not easy to achieve the target, cut down 50% of the CO2 emission.

Offline Urdu Nastaleeq Optical Character Recognition Based on Stacked Denoising Autoencoder

Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.

Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.

Teaching Model Integrating Online and Offline in Real Time ——An Exploration of New Teaching Mode for Regular Epidemic Prevention and Control

During epidemic,students in medium-risk or high-risk areas are unable to return to school on time.In response to this new challenge,there is an urgent need to create a new teaching mode to offer on-line courses to those absent from the offline classes,and we propose a model integrating online and offline teaching.It is based on“dual-camera”method,which allows off-campus students to virtually build up a physical classroom scenario on campus through computers and mobile phones.Using this model,students can participate in class remotely.In order to enhance the engagement of off-campus online students,emphasis is placed on interactive teaching.Teachers are required to design their teaching in advance and to work in collaboration with multiple departments,then using information technology and suitable teaching methods to enable students to participate in physical classroom teaching.This model has been tested in practice and has been successful in meeting the challenges.Finally,4 areas for improvement and refinement are identified.