基于拟剧理论探讨网络人设现象

社交媒体平台不仅为公众建构人设提供了广阔空间,而且进一步丰富了公众对于“自我”的多元化认知。无论是明星群体还是公众个人,均可借助文本、图片、视频等形式在社交媒体平台建构理想化的人设,并通过前台表演、后台管理等一系列“印象管理”行为维持人设,以此向其他公众呈现“理想自我”,这一现象的形成过程与拟剧理论中的印象管理极为契合。文章借助拟剧理论分析网络人设现象,希望读者能对网络人设有一个清晰而正确的认知。

网络人设的符号化建构、表演及反思

网络人设因其具备符号价值成为当下一种流行广泛的网络文化消费现象,其符号化建构并非只是简单的网络娱乐,更是一种吸引消费者和粉丝的营销手段,具有操控性、“表演”等特征。网络人设来源于现实身份,但又区别于现实身份,其在网络空间中的符号表演带有很大的伪饰性与逐利性。在当代日益发达的消费社会中,网络人设是新媒体传播背景下的一种普遍现象和客观现实,有其存在的合理性,但不应忽略其逐利性弊端,而应加强监管和治理,倡导符合社会发展要求、弘扬社会正能量的网络文化。

秩序重建、剧场表演及消费主义——网络人设的传播学解读

随着网络移动终端的普及,普通大众有了自己的网络人设。网络广场上,那些个性鲜明、风格独特的人设易引起群体共振,形成网络狂欢现象。在对热点人设的解读、模仿和二次传播中,主流话语被颠覆,网络秩序被重构,网络圈层呈现,社会"亚文化"生成。人设的本质是剧场表演,通过符号方式展现。人设的最终目的是达成社会价值的实现,在实现过程中个体异化为文化工业社会中消费的"物"。在人设制造流程中,人设的生产者、消费者或商家各取所需,共同完成了消费主义的网络合谋。

网络互动中人设的传播与反思

英国学者理查德·戴尔在《明星》一书中提出明星是一种“人格消费”。在娱乐化浪潮的冲击下,网络人设建构的动态互动叙事也在商业裹挟下呈现。网络人设营销从最初的影视形象延伸、微博图文营销、平台剪辑引导,到实时的直播互动,单一化的媒介手段和静态化的媒介叙事向动态化转变。基于此,本文探讨网络直播互动视角下公众人物人设的传播,并思考网络异化当下可行的秩序重建路径。

网络“人设”传播:符号资本的媒介建构

伴随信息技术和互联网技术的飞速发展,人们的生活方式、消费方式、社会交往方式甚至话语体系等都不断被技术重构。在网络文化空间中,“人设”一词成为近年来较为热门的典型网络词汇,作为一种被设定的概念化符号形象,它通过特定人物的媒介展演而吸引不同的受众群体,在无形中进入大众消费端,进而转化为符号资本与经济价值。网络“人设”符号的出现,是数字经济时代符号消费产业对于网络文化环境强有力的适应和经营。作为一种新的网络文化症候,它的形成与发展有着鲜明特征,“人设”符号消费虽鲜明体现于精神层面,却能在资本市场和受众市场形成强大力量。本文将对网络“人设”从概念到符号资本的媒介建构过程进行深入阐释,通过对“人设”符号的理论阐释有助于我们更好地了解当下网络文化症候、媒介舆论环境与社会文化心理。

涂层概念视域下网络“人设”的传播策略与危机表征

网络社会中的多元"人设"是涂层者利用炫化符号进行"形象展演"的过程,这不仅改变了自我审视的方式,还推动着网络人设形象的进一步传播。本文借助"涂层"概念对网络人设的传播策略和危机表征加以探析。研究认为,网络人设主体为了迎合大众的消遣娱乐心理,通过陌生化叙事、媒介建构、角色仿真等方式不断强化特定人设,网络人设逐渐变成涂层社会的"焦点"。然而,在虚拟网络技术所营造的后真相时代,网络人设如同"善意的谎言",维护了形象却失去了本真。自我不再是真实存在的实在生命,而是成为充满象征性、失去主体的漂浮的"能指"。

OPTIMIZATION METHOD ON IMPELLER MERIDIONAL CONTOUR AND 3D BLADE

An optimization method for 3D blade and meridional contour of centrifugal or mixed-flow impeller based on the 3D viscous computational fluid dynamics （CFD） analysis is proposed. The blade is indirectly parameterized using the angular momentum and calculated by inverse design method. The design variables are separated into two categories： the meridional contour design vari- ables and the blade design variables. Firstly, only the blade is optimized using genetic algorithm with the meridional contour remained constant. The artificial neural network （ANN） techniques with the training sample data schemed according to design of experiment theory are adopted to construct the response relation between the blade design variables and the impeller performance. Then, based on the ANN approximated relation between the meridional contour design variables and impeller per- formance, the meridional contour is optimized. Fewer design variables and less calculation effort is required in this method that may be widely used in the optimization of three-dimension impellers. An optimized impeller in a mixed-flow pump, where the head and the efficiency are enhanced by 12.9% and 4.5% respectively, confirms the validity of this newly proposed method.

Review of the Development of China's Mobile Broadband Networks

The year of 2014 saw the beginning of China's LTE,which marks that China has become one of the major engines for the global LTE development.China dazzled in the construction of LTE networks,subscribers,and industry chain.However,the policy formulated around TD-LTE also put China in predicament and brought it many challenges.With this article,we are going to put China's market for mobile broadband networks into perspective,focusing on the development of China's LTE market,challenges that encountered,and the research in this area in the years to come.Besides,in regards to the problems that already appeared,we will,from policy-making,industry-level,and technological points of view,offer our suggestions on how China should do to make this market robust.

Artificial neural network based inverse design method for circular sliding slopes

Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

An optimized control of ventilation in coal mines based on artificial neural network

According to the nonlinear and time dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of coal mine ventilation system are introduced, and the principle of controlling a ventilation fan is also explained in detail. The artificial neutral network method is used to calculate the wind quantity needed by work spots in coal mine on the basis of the data collected by the system, including ventilation conditions, environmental temperatures, humidity, coal dust and the contents of all kinds of poisonous and harmful gases. Then the speed of ventilation fan is controlled according to the required wind which is determined by an overall integration of data. A neural network method is presented for overall optimized solution or the genetic algorithm of simulated annealing.

Experimental study of laser cladding process and prediction of process parameters by artificial neural network(ANN)

Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.

Three-Dimensional Ocean Sensor Networks:A Survey

The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.

A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification

ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.

Illumination of parameter contributions on uneven break： phenomenon in underground stoping mines

One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.

A study on TCP performance of crowdsourced live streaming

The prevalence of smart phone and improvement of wireless net promote the usage of crowdsourced live streaming,where individual users act as live streaming sources to broadcast themselves online. Characterizing the performance and identifying its bottleneck in such systems can shed light on the system design and performance optimization. TCP performance of a commercial crowdsourced live streaming system is examined by analyzing packet-level traces collected at streaming servers. TCP stalls that heavily hurt the Qo E of user have been identified. In particular,the TCP stalls account for as much as 31. 6% of the flow completion time for upload flows and result in abandonment of upload on the corresponding channels. Stalls caused by timeout retransmissions are further dissected and timeout retransmission characteristics are revealed to be dependent on the video encoding methods. These findings provide new insights in crowdsourced live streaming systems and can guide designers to improve the TCP efficiency.

Uncalibrated visual servoing design for competitive networked robots

In this paper,a method with an eye-in-hand configuration is developed to hit targets during visual tracking for the TLS(Tele-Light Saber) game.It is not necessary to calibrate camera parameters and predict the trajectory of the moving object.Firstly,the expression of the image Jacobian matrix for the eye-in-hand configuration is proposed,and then an update law is designed to estimate the image Jacobian online.Furthermore,a control scheme is presented and the Lyapunov method is employed to prove asymptotic convergence of image errors.No assumption for the moving objects is needed.Finally,both simulation and experimental results are shown to support the approach in this paper.

Research and Analysis of the Parallel Computer Network Reliability

With the continuous development of network communication technology and computer technology, parallel computer network applications becoming more widely, its reliability has attracted more attention on researcher. This paper gives a introduction to a simple computer network, given the reliability of the design criteria for computer network analysis, and finally through the examples to illustrate the computer network hardware and software reliability.

Simulating the Inverse Kinematic Model of a Robot through Artificial Neural Networks： Complementing the Teaching of Robotics

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs （artificial neural networks） in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot.

Learning adaptive receptive fields for deep image parsing networks

In this paper,we introduce a novel approach to automatically regulate receptive fields in deep image parsing networks.Unlike previous work which placed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels,our approach uses two affine transformation layers in the network’s backbone and operates on feature maps.Feature maps are inflated or shrunk by the new layer,thereby changing the receptive fields in the following layers.By use of end-to-end training,the whole framework is data-driven,without laborious manual intervention.The proposed method is generic across datasets and different tasks.We have conducted extensive experiments on both general image parsing tasks,and face parsing tasks as concrete examples,to demonstrate the method’s superior ability to regulate over manual designs.