基于深度学习的公共建筑像素施工图空间识别

像素级图纸空间识别在自动审图、图纸翻模应用中起到关键作用.已有识别方案主要针对户型图,基于语义分割直接识别空间.公共建筑施工图有更多干扰线和图纸元素、更高分辨率及更多空间类型,高分辨率使模型难以获取图纸的全局信息,空间类型的多样性导致无法确定空间类型的范围,已有空间识别方案难以直接迁移.为了识别公共建筑施工图中的空间,...     

A deep generic to specific recognition model for group membership analysis using non-verbal cues

Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of the non-verbal behaviors and how this can be utilized for analysis of group membership, i.e., recognizing which group each individual is part of. This paper presents a novel Support Vector Machine (SVM) based Deep Specific Recognition Model (DeepSRM) that is learned based on a generic recognition model. The generic recognition model refers to the model trained with data across different conditions, i.e., when people are watching movies of different types. Although the generic recognition model can provide a baseline for the recognition model trained for each specific condition, the different behaviors people exhibit in different conditions limit the recognition performance of the generic model. Therefore, the specific recognition model is proposed for each condition separately and built on top of the generic recognition model. A number of experiments are conducted using a database aiming to study group analysis while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities.     

A model-based recognition engine for sketched diagrams

Many of today's recognition approaches for hand-drawn sketches are feature-based, which is conceptually similar to the recognition of hand-written text. While very suitable for the latter (and more tasks, e.g., for entering gestures as commands), such approaches do not easily allow for clustering and segmentation of strokes, which is crucial to their recognition. This results in applications which do not feel natural but impose artificial restrictions on the user regarding how sketches and single components (shapes) are to be drawn.This paper proposes a concept and architecture for a generic geometry-based recognizer. It is designed for the mentioned issue of clustering and segmentation. All strokes are fed into independent preprocessors called transformers that process and abstract the strokes. The result of the transformers is stored in models. Each model is responsible for a certain type of primitive, e.g., a line or an arc. The advantage of models is that different interpretations of a stroke exist in parallel, and there is no need to rate or sort these interpretations. The recognition of a component in the drawing is then decomposed into the recognition of its primitives that can be directly queried for in the models. Finally, the identified primitives are assembled to the complete component. This process is directed by an automatically computed search plan, which exhibits shape characteristics in order to ensure an efficient recognition.In several case studies the applicability and generality of the proposed approach is shown, as very different types of components can be recognized. Furthermore, the proposed approach is part of a complete system for sketch understanding. This system not only recognizes single components, but can also understand sketched diagrams as a whole, and can resolve ambiguities by syntactical and semantical analysis. A user study was conducted to obtain recognition rates and runtime data of our recognizer.     

Designing a two-rank acceptance sampling plan for quality inspection of geospatial data products

To address the disadvantages of classical sampling plans designed for traditional industrial products, we originally propose a two-rank acceptance sampling plan (TRASP) for the inspection of geospatial data outputs based on the acceptance quality level (AQL). The first rank sampling plan is to inspect the lot consisting of map sheets, and the second is to inspect the lot consisting of features in an individual map sheet. The TRASP design is formulated as an optimization problem with respect to sample size and acceptance number, which covers two lot size cases. The first case is for a small lot size with nonconformities being modeled by a hypergeometric distribution function, and the second is for a larger lot size with nonconformities being modeled by a Poisson distribution function. The proposed TRASP is illustrated through two empirical case studies. Our analysis demonstrates that: (1) the proposed TRASP provides a general approach for quality inspection of geospatial data outputs consisting of non-uniform items and (2) the proposed acceptance sampling plan based on TRASP performs better than other classical sampling plans. It overcomes the drawbacks of percent sampling, i.e., “strictness for large lot size, toleration for small lot size,” and those of a national standard used specifically for industrial outputs, i.e., “lots with different sizes corresponding to the same sampling plan.”     

Designing function blocks for distributed process planning and adaptive control

The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.     

A Model-Based Method for Rotation Invariant Texture Classification

This paper presents a new model-based approach for texture classification which is rotation invariant, i.e., the recognition accuracy is not affected if the orientation of the test texture is different from the orientation of the training samples. The method uses three statistical features, two of which are obtained from a new parametric model of the image called a ``circular symmetric autoregressive model.' Two of the proposed features have physical interpretation in terms of the roughness and directionality of the texture. The results of several classification experiments on differently oriented samples of natural textures including both microtextures and macrotextures are presented.     

A plan-based agent architecture for interpreting natural language dialogue

This paper describes a plan-based agent architecture for modelling NL cooperative dialogue; in particular, the paper focuses on the interpretation of dialogue and on the explanation of its coherence by means of the recognition of the speakers' underlying intentions. The approach we propose makes it possible to analyze an explain in a uniform way several apparently unrelated linguistic phenomena, which have been often studied separately and treated via ad-hoc methods in the models of dialogue presented in the literature. Our model of linguistic interaction is based on the idea that dialogue can be seen as any other interaction among agents: therefore, domain-level and linguistic actions are treated in a similar way.Our agent architecture is based on a two-level representation of the knowledge about acting: at the metalevel, the agent modelling (AM) plans describe the recipes for plan formation and execution (they are a declarative representation of a reactive planner); at the object level, the domain and communicative actions are defined. The AM plans are used to identify the goals underlying the actions performed by an observed agent; the recognized plans constitute the dialogue context, where the intentions of all participants are stored in a structured way, in order to be used in the interpretation of the subsequent dialogue turns.     

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.     

基于限定的规划识别问题求解

该文把McCarthy的限定理论同规划识别结合起来，在限定中研究规划识别问题，证明了在一定的限制下，由观察到的现象求出的最小规划集与对这些现象作限定获得的解集是一样的，以此为基础，文中提出了一种用限定求解规划识别问题的方式，这种方法把Kautz提出的规划识别表示形式做了某些改变，求解的过程中把二阶限定的表示形式转化为一阶形式，这种一阶形式的限定结果可以用逐点限定的方法直接求得，因为利用了逐点限定的这一特点，该文的方法对限定的计算过程中可以用机器自动完成。     

智能规划中面向简单偏好的高效求解方法

智能规划(AI planning)简称规划,是人工智能领域的一个重要分支,在各领域均有广泛应用,如工厂车间作业调度、物资运输调度、机器人动作规划以及航空航天任务规划等.传统智能规划要求规划解(动作序列)必须最终实现整个目标集合,这种目标一般被称为硬目标(hard goal).然而,许多实际问题中,求解的重点并不只是尽快实现目标以及尽量减少动作序列产生的代价,还需考虑其他因素,如资源消耗或时间约束等.为此,简单偏好(也称软目标soft goal)的概念应运而生.与硬目标相反,简单偏好是可以违背的.本质上,简单偏好用于衡量规划解质量的优劣,而不会影响规划解是否存在.现有关于简单偏好的研究进展缓慢,在规划解质量方面不尽如人意即求得的规划解与最优解的差距较大.提出了一种求解简单偏好的高效规划方法,将简单偏好表达为经典规划(classical planning)模型的一部分,并利用SMT (satisfiability modulo theories)求解器识别多个简单偏好之间的各种关系,从而约简简单偏好集,减轻规划器的求解负担.该方法的主要优势在于:一方面,提前对简单偏好集进行裁剪,在一定程度...     

Optimizing large join queries using a graph-based approach

Although many query tree optimization strategies have been proposed in the literature, there still is a lack of a formal and complete representation of all possible permutations of query operations (i.e., execution plans) in a uniform manner. A graph-theoretic approach presented in the paper provides a sound mathematical basis for representing a query and searching for an execution plan. In this graph model, a node represents an operation and a directed edge between two nodes indicates the older of executing these two operations in an execution plan. Each node is associated with a weight and so is an edge. The weight is an expression containing optimization required parameters, such as relation size, tuple size, join selectivity factors. All possible execution plans are representable in this graph and each spanning tree of the graph becomes an execution plan. It is a general model which can be used in the optimizer of a DBMS for internal query representation. On the basis of this model, we devise an algorithm that finds a near optimal execution plan using only polynomial time. The algorithm is compared with a few other popular optimization methods. Experiments show that the proposed algorithm is superior to the others under most circumstances     

A formal description of tactical plan recognition

Plan recognition can roughly be described as the problem of finding the plan(s) underlying the observed behaviour of agent(s). Of course, usually, the observed behaviour and available background knowledge does not determine the underlying plan, and therefore one can typically at best generate (reasonable) plan hypotheses. Traditionally, plan recognition has been studied, formalized and implemented in areas like story understanding and user modelling. In this paper, we propose a formal definition of tactical plan recognition, i.e. the recognition of enemy plans. We will focus on military applications, where this task of tactical plan recognition is crucial, but this task is relevant for every application where one has to deal with intelligent adversial agents.Tactical plan recognition differs from traditional plan recognition in a number of ways. For example, an enemy will often try to avoid making his plans known. We will not pay much explicit attention to this feature. We will focus on another important characteristic feature of tactical plan recognition, namely that the identity of the observed enemy objects, for which plans are to be recognized, may be unknown. A consequence of this is that it is typically not known which observations originate from the same objects.Our formalization of plan recognition is based on classical abduction. The concepts of classical abduction can readily be applied to plan recognizers for identified observations, as has been done by Lin and Goebel [18] and Bauer and Paul [7]. However, for tactical plan recognition some adaptations have to be made. Here the plan recognizer will not only have to generate plan hypotheses, but also assignment hypotheses, which correspond to formal links of objects to observations. A choice for an assignment is essentially a decision concerning the question which observations originate from the same objects.For observations with stochastic variables the probability of an assignment hypothesis is calculated, rather than the probability of the plan hypotheses. For this, Reid’s multiple hypothesis tracking formula can be adapted to calculate the assignment hypothesis probability.     

Enhanced graph-based dimensionality reduction with repulsion Laplaceans

Graph-based methods for linear dimensionality reduction have recently attracted much attention and research efforts. The main goal of these methods is to preserve the properties of a graph representing the affinity between data points in local neighborhoods of the high-dimensional space. It has been observed that, in general, supervised graph-methods outperform their unsupervised peers in various classification tasks. Supervised graphs are typically constructed by allowing two nodes to be adjacent only if they are of the same class. However, such graphs are oblivious to the proximity of data from different classes. In this paper, we propose a novel methodology which builds on ‘repulsion graphs’, i.e., graphs that model undesirable proximity between points. The main idea is to repel points from different classes that are close by in the input high-dimensional space. The proposed methodology is generic and can be applied to any graph-based method for linear dimensionality reduction. We provide ample experimental evidence in the context of face recognition, which shows that the proposed methodology (i) offers significant performance improvement to various graph-based methods and (ii) outperforms existing solutions relying on repulsion forces.     

Animated interface agent applying atms-based multimodal input interpretation

Two requirements should be met in order to develop a practical multimodal interface system , i . e ., ( 1 ) integration of delayed arrival of data and ( 2 ) elimination of ambiguity in recognition results of each modality . This paper presents an efficient and generic methodology for interpretation of multimodal input to satisfy these requirements . The proposed methodology can integrate delayed - arrival data satisfactorily and efficiently interpret multimodal input that contains ambiguity . In the input interpretation the multimodal interpretation process is regarded as hypothetical reasoning , and the control mechanismof interpretation is formalized by applying the assumption - based truth maintenance system ( ATMS ). The proposed method is applied to an interface agent system that accepts multimodal input consisting of voice and direct indication gesture on a touch display . The systemcommunicates to the user through a human - like interface agent's three - dimensional motion image with facial expressions , gestures , and a synthesized voice .     

Face recognition using Intrinsicfaces

In this paper, we propose a novel face model, called intrinsic face model. Under this model, each face image is divided into three components, i.e., facial commonness difference, individuality difference and intrapersonal difference, to characterize some certain differences conveyed by this image. Then, a new supervised dimensionality reduction technique coined Intrinsic Discriminant Analysis (IDA) is developed. Intrinsic Discriminant Analysis tries to best classify different face images by maximizing the individuality difference, while minimizing the intrapersonal difference. By using perturbation technique to tackle the singularity problem of IDA which occurs frequently in face recognition, we obtain a new appearance-based face recognition method called Intrinsicfaces. A series of experiments to compare our proposed approach with other dimensionality reduction methods are tested on three well-known face databases. Experimental results demonstrate the efficacy of the proposed Intrinsicfaces approach in face recognition.     

A formal approach to automating conceptual structural design,part II: Application to floor framing generation

The function of the structural system in a building is to transfer gravity as well as lateral loads from their points of origin to the ground. Floor framing generation involves providing a path to transfer the gravity loads to the ground through various structural elements in an architectural plan while meeting the requirements imposed by other entities, such as the architect, the mechanical engineer, and the contractor, involved in the design/construct process. In this paper a formal approach for generating floor framing plans for steel office buildings is presented. We describe the knowledge and the reasoning behind a computer system, FFG (floor framing generator), which generates floor framing schemes for steel office buildings that are rectangular in plan and have a single service core. Constraints arising from structural as well as exogenous considerations are enumerated and their effect on framing schemes is identified. We also elaborate on the evaluation mechanism for ranking alternative schemes, in addition to providing details of the computer implementation.     

On the interaction between plan recognition and intelligent interfaces

Plan recognition is an active research area in automatic reasoning, as well as a promising approach to engineering interfaces that can exploit models of user's plans and goals. Much research in the field has focused on the development of plan recognition algorithms to support particular user/system interactions, such as found in naturally occurring dialogues. However, two questions have typically remained unexamined: 1) exactly what kind of interface tasks can knowledge of a user's plans be used to support across communication modalities, and 2) how can such tasks in turn constrain development of plan recognition algorithms? In this paper we present a concrete exploration of these issues. In particular, we provide an assessment of plan recognition, with respect to the use of plan recognition in enhancing user interfaces. We clarify how use of a user model containing plans makes interfaces more intelligent and interactive (by providing an intelligent assistant that supports such tasks as advice generation, task completion, context-sensitive responses, error detection and recovery). We then show how interface tasks in turn provide constraints that must be satisfied in order for any plan recognizer to construct and represent a plan in ways that efficiently support these tasks. Finally, we survey how interfaces are fundamentally limited by current plan recognition approaches, and use these limitations to identify and motivate current research. Our research is developed in the context of CHECS, a plan-based design interface.