Dynamic Granularity Research Articles

A Method for Indoor Space Lighting Layout Design Based on Variable Granularity Co-evolution

This paper addresses the essential but tricky project of indoor area lighting graph, underscoring its importance in reaching energy efficiency, aesthetic attraction, and useful illumination. traditional lights design methods regularly fall short in adaptability and optimization, main to suboptimal illumination and electricity inefficiency. To triumph over these limitations, we suggest a unique approach employing a variable granularity co-evolutionary set of rules for indoor area lighting plan diagram. Our method initiates with a mathematical modeling of the indoor space, setting more than one optimization targets like lighting fixtures efficiency, cost-effectiveness, and aesthetic issues. The core of our method lies within the integration of a variable granularity method with a co-evolutionary set of rules. This amalgamation enables dynamic granularity modifications based on evolutionary progress, enhancing the search efficacy throughout multiple goals. Next steps include actual-time environmental tracking and comments integration, permitting adaptive lighting fixtures diagram modifications. Experimental results reveal the superiority of our approach over conventional techniques, showcasing huge upgrades in lighting fixtures best and energy intake. The proposed technique's adaptability and efficiency mark a widespread advancement in wise lighting fixtures diagram.

Dynamic granularity selection based on local weighted accuracy and local likelihood ratio

Granular computing aims to develop a granular view for interpreting and solving problems, in which granularity selection is a key problem and has received extensive attention in recent years. Existing studies select the same granularity for all samples. In fact, different samples may prefer to different granularities. To address this issue, dynamic granularity selection is proposed in this paper. Namely, granularity selection is considered with respect to specific sample. Two indices, denoted as local weighted accuracy and local likelihood ratio, are introduced to compute the weight of granularity. Subsequently, an algorithm called DGS−LWA-LLS is given for dynamic granularity selection, in which the granularity with the largest weight is considered to be optimal The weight of granularity is related to specific sample, thus the weights of a granularity may be different with different samples. Consequently, different granularities will be selected with respect to different samples. Experiments were carried out based on neighborhood granularity to explain the necessity of granularity selection and to validate the rationality and effectiveness of DGS−LWA-LLS.

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.

A matrix factorization based dynamic granularity recommendation with three-way decisions

Recommender systems (RSs) are effective technologies and tools used to deal with the problems of information overload, and have been developed rapidly in nearly two decades. In this paper, with the consideration of uncertain and multi-level characteristic of recommendation information (RI), we combine three-way decisions with granular computing, and build a novel dynamic three-way recommendation model to address the limitations of static two-way recommendation. First, we propose a dynamic three-way granularity recommendation (DTWGR), which adopts a top-down dynamic recommendation strategy based on the granular structure of RI. Second, we introduce a matrix factorization framework and further utilize three methods: SVD, SVD++ and NMF, to construct the granular structure of RI. In addition, three extended recommendation algorithms, namely, DTWGR-SVD, DTWGRSVD ++ and DTWGR-NMF, are proposed by combining DTWGR with matrix factorization. The recommendation cost caused by misclassification cost and teaching cost, is carefully investigated in our work. Finally, experimental results of three well-known Movielens data sets validate the effectiveness and reliability of our proposed model.

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

Optimization Strategies Oriented to Loop Characteristics in Software Thread Level Speculation Systems

Thread level speculation provides not only a simple parallel programming model, but also an effective mechanism for thread-level parallelism exploitation. The performance of software speculative parallel models is limited by high global overheads caused by different types of loops. These loops usually have different characteristics of dependencies and different requirements of optimization strategies. In this paper, we propose three comprehensive optimization techniques to reduce different factors of global overheads, aiming at requirements from different types of loops. Inter-thread fetching can reduce the high mis-speculation rate of the loops with frequent dependencies and out-of-order committing can reduce the control overhead of the loops with infrequent dependencies, while enhanced dynamic task granularity resizing can reduce the control overhead and optimize the global overhead of the loops with changing characteristics of dependencies. All these three optimization techniques have been implemented in HEUSPEC, a software TLS system. Experimental results indicate that they can satisfy the demands from different groups of benchmarks. The combination of these techniques can improve the performance of all benchmarks and reach a higher average speedup.

Dynamic granularity of imaging systems.

Imaging systems that include a specific source, imaging concept, geometry, and detector have unique properties such as signal-to-noise ratio, dynamic range, spatial resolution, distortions, and contrast. Some of these properties are inherently connected, particularly dynamic range and spatial resolution. It must be emphasized that spatial resolution is not a single number but must be seen in the context of dynamic range and consequently is better described by a function or distribution. We introduce the "dynamic granularity" G(dyn) as a standardized, objective relation between a detector's spatial resolution (granularity) and dynamic range for complex imaging systems in a given environment rather than the widely found characterization of detectors such as cameras or films by themselves. This relation can partly be explained through consideration of the signal's photon statistics, background noise, and detector sensitivity, but a comprehensive description including some unpredictable data such as dust, damages, or an unknown spectral distribution will ultimately have to be based on measurements. Measured dynamic granularities can be objectively used to assess the limits of an imaging system's performance including all contributing noise sources and to qualify the influence of alternative components within an imaging system. This article explains the construction criteria to formulate a dynamic granularity and compares measured dynamic granularities for different detectors used in the X-ray backlighting scheme employed at Sandia's Z-Backlighter facility.

Access granularity control of multichannel random access in next-generation wireless LANs

As the next-generation Wireless LANs (WLANs) will provide the ubiquitous high-data-rate network coverage, the traditional contention-based Medium Access Control (MAC) scheme may be unable to fulfill the requirement of efficient channel access. To address this problem, several research works have proposed the random access systems combined with Orthogonal Frequency Division Multiplex Access (OFDMA) technology. Access granularity control is a crucial issue in this combination. Specifically, this issue focuses on how to tune the subchannel bandwidth and the number of accessible subchannels towards the maximum channel utilization. This paper analyzes access granularity control in an OFDMA system that adopts a multichannel Carrier Sensing Multiple Access (CSMA) MAC and resolves contention by the frequency-domain backoff. The theoretical analysis verifies the significance of access granularity control. In addition, the simulation experiments demonstrate that the proposed dynamic access granularity control algorithms notably outperform the traditional ones that divide channel band statically and adjust the number of accessible subchannels empirically.

The dynamic granularity memory system

Chip multiprocessors enable continued performance scaling with increasingly many cores per chip. As the throughput of computation outpaces available memory bandwidth, however, the system bottleneck will shift to main memory. We present a memory system, the dynamic granularity memory system (DGMS), which avoids unnecessary data transfers, saves power, and improves system performance by dynamically changing between fine and coarse-grained memory accesses. DGMS predicts memory access granularities dynamically in hardware, and does not require software or OS support. The dynamic operation of DGMS gives it superior ease of implementation and power efficiency relative to prior multi-granularity memory systems, while maintaining comparable levels of system performance.

Attribute reduction algorithm based on dynamic quotient granularity

Rough Set(RS) theory is an effective approach of imprecision,vagueness and incompleteness in classification analysis and knowledge discovery.Rough set based on boundary region was dynamically analyzed from the coarser degree of granularity.Dynamic quotient granularity was defined by the principle of attribute connection,and a new attribute reduction algorithm based on the coarser degree of granularity principle was proposed.The optimal reduction set can be selected from all reduction set with algorithm for dynamic quotient granularity.It abandons the tradition to ask the core first,and then chooses the optimal reduction set.The validity of proposed granularity computing algorithm is proved by the application of practical database.Moreover,it can be used for granularity computation of knowledge.

A Dynamic Granularity Control Algorithm for Avoiding TCP Deadlocks over Congested ATM Networks

Conventional TCP suffers throughput deadlocks over congested ATM (Asynchronous Transfer Mode) networks due to retransmission timeouts caused by cell loss at ATM level under heavy congestion. A modified congestion control algorithm for TCP is proposed to improve the effective throughput of IP over ATM UBR even in a situation such that an external CBR pressure exist at the same port of the ATM switch. When TCP faces congestion then TCP will reset its retransmission timer. In the proposed algorithm, the MSS (maximum segment size) is reduced from 9140 bytes, the recomended value in RFC 1612, at the same time. When TCP is in fast retransmit machine phase, the MSS is reduced to 512 bytes. A considerable improvement in the effective throughput of TCP has been achieved by these two modifications under relatively small switch buffer size and high CBR stream pressure. The idea, called “Dynamic Granularity Control (DGC)”, have been implemented and evaluated on the Linux kernel.

Using the run-time sizes of data structures to guide parallel-thread creation

Dynamic granularity estimation is a new technique for automatically identifying expressions in functional languages for parallel evaluation. Expressions with little computation relative to thread-creation costs should evaluate sequentially for maximum performance. Static identification of such threads is however difficult. Therefore, dynamic granularity estimation has compile-time and run-time components: Abstract interpretation statically identifies functions whose complexity depends on data structure sizes; the run-time system maintains approximations to these sizes. Compiler-inserted checks consult this size information to make thread creation decisions dynamically. We describe dynamic granularity estimation for a list-based functional language. Extension to general recursive data structures and imperative operations is possible. Performance measurements of dynamic granularity estimation in a parallel ML implementation on a shared-memory machine demonstrate the possibility of large reductions (>20%) in execution time.