Hard Computing VS Soft Computing

Hard and Soft Computing,

There are many tools in mathematics, but all of them can divide into Hard and Soft two kinds. Such as

A) Traditional physical is focus on Hard Computing, whereas

B) In 17th there are emerge statistic in mathematics, present, a new paradigm break out, it is fuzziness.

Why will emerge randomness (statistic) and fuzziness? The reason is Complex Problem. In Classical Mechanics, we can easy modeling a object move. However when we want to calculate a coin weather a head or tail, there are a great many of constraint to effect it. Indeed, we can not clearly understand the overall constraint, thus, we propose a new approach that statistic and get an approximate answer.

Hard Computing against Soft Computing,

Hard and Soft Computing is two different attitude of science. Many physical scientists are not support using soft computing in scientific problem solving. If a human is a computer, which will be a soft computer rather than a hard computer, there are many reasonable instances, such as we read a book, remember the entire story is not necessary, and just keep the key point is enough. In soft computing, it has a very powerful at dealing with complex problem, with Granular and Fuzzy mechanism. In common sense, we only finish the job, no matter how accuracy or precise it is. Thus, in many our real word problem, we just want to have an approximate answer.

From Hard to Soft Computing,

There is because of we have knowledge, that we can solve the problem. In our common sense knowledge, there is much knowledge with imprecise, incomplete, and not totally reliable. Soft Computing have many application, I remember that GuoLiang Chen academician visit our school, and he said that, he exploit Artificial Neural Network (ANN) in dealing with the high dimension and complex relation with learning character of ANN.

Many systems have not learning mechanism, whereas in Knowledge-Based System, learning is a very useful factor in application domain. Such as, you are a boss in a store. you want to have a function which can extract knowledge (rules) form large data set. That you need the learning function, in this requirement, ANN is useless, and should make use of fuzzy rule-based system.

Conclusion,

There are also many reasons why we should shift from hard computing to soft computing. The Machine Leaning is the most vivid instance in application. And it have widely use in many industry, such as bank, business support, finger recognize and so on. In words, Soft Computing is a good tools.

### Soft Shadow in Computer Graphics and Rendering Techniques Soft shadows are an essential aspect of realistic rendering in computer graphics. Unlike hard shadows that have sharp boundaries, soft shadows exhibit gradual transitions between fully lit areas and completely shaded regions. This effect occurs because real-world light sources possess non-zero size rather than being point-like. The creation of soft shadows involves simulating how light interacts with objects within a scene more accurately by considering not only direct illumination but also indirect contributions through multiple bounces or occlusions caused by other elements present nearby[^1]. One common approach to achieve this is via **penumbra**, which refers specifically to partially illuminated zones surrounding umbrae—the darkest parts where no part of the source can be seen directly due to obstruction. To implement such effects efficiently while maintaining visual quality, various algorithms exist: #### Percentage-Closer Filtering (PCF) A widely used technique called PCF averages samples taken around each pixel on shadow maps during depth testing against corresponding points projected onto them from potential blockers' positions relative to lights’ perspectives. By doing so, smoother edges emerge without requiring excessive sampling rates per fragment processed at runtime costs. ```cpp float getShadowFactor(sampler2D shadowMap, vec4 fragPosLightSpace) { // Perform perspective divide vec3 projCoords = fragPosLightSpace.xyz / fragPosLightSpace.w; float closestDepth = texture(shadowMap, projCoords.xy).r; float currentDepth = projCoords.z; vec2 texelSize = 1.0 / textureSize(shadowMap, 0); float pcFResult = 0.0; for(int x = -1; x <= 1; ++x) { for(int y = -1; y <= 1; ++y) { float pcfDepth = texture(shadowMap, projCoords.xy + vec2(x,y)*texelSize).r; pcFResult += currentDepth > pcfDepth ? 1.0 : 0.0; } } return pcFResult /= 9.0; } ``` This code snippet demonstrates basic implementation details behind percentage-closer filtering when working alongside shadow mapping strategies employed commonly today across many applications ranging from video games engines down towards specialized research software packages alike[^2]. Another advanced method mentioned previously includes computing volumetric light transport directly inside screenspace buffers instead of relying solely upon traditional ray tracing methods operating over entire scenes repeatedly until convergence criteria met satisfactorily enough visually speaking—this allows developers greater flexibility regarding performance versus fidelity tradeoffs depending upon target platforms involved here too[^3].
评论 1
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值