Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

We propose a generative model based method for recovering both the shape and the reflectance of the surface(s) of a scene from multiple images, assuming that illumination conditions and cameras calibration are known in advance. Based on a variational framework and via gradient descents, the algorithm minimizes simultaneously and consistently a global cost functional with respect to both shape and reflectance. The motivations for our approach are threefold. (1) Contrary to previous works which mainly consider specific individual scenarios, our method applies indiscriminately to a number of classical scenarios; in particular it works for classical stereovision, multiview photometric stereo and multiview shape from shading. It works with changing as well as static illumination. (2) Our approach naturally combines stereo, silhouette and shading cues in a single framework. (3) Moreover, unlike most previous methods dealing with only Lambertian surfaces, the proposed method considers general dichromatic surfaces. We verify the method using various synthetic and real data sets.     

Device reliability under electrical stress and photo response of oxide TFTs

Abstract— The stability of oxide TFTs has been the main focus of this research and is probably the most crucial requirement for the successful application to flat‐panel displays. Although the high Fermi level of oxide semiconductors makes TFTs basically stable under electrical stress, the device reliability under diverse variations of electrical stress is affected by materials such as active semiconductors and gate insulators, processes for the formation of back/front channels and passivation layers, and device configurations among other things. How these factors affect the device reliability have been investigated and a review of the stability is presented. In addition, several categories of the light instability of oxide TFTs is presented and the origin is discussed.     

Adaptive product tracking in RFID-enabled large-scale supply chain

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.     

Intelligent adaptive process control using dynamic deadband for semiconductor manufacturing

This paper proposes an efficient control method to minimize process error and to reduce process variance in semiconductor manufacturing. The photolithography (photo) process forms a complex semiconductor circuit and is important for quality. Obstacles to the process include the facility itself, vibration, wear and tear, product/process changes and environmental influences. Control methodologies being currently used to address these issues often amplify the variation of the process by failing to perform adequate process control. Therefore, this paper proposes an effective process control method to reduce process variance by quickly detecting and identifying process disturbances and accurately reflecting the degree of change to process control. This study proposes dynamic deadband control that uses a region (band) to detect the status of a process change. It adjusts the process control based on the changes detected. In this research, the semiconductor manufacturing company is supported to perform control that is more precise and reduces fluctuations by producing products of uniform quality. In addition, it can contribute to yield due to the quality incentive and increased process control of semiconductor manufacturing.     

A data-driven generic simulation model for logistics-embedded assembly manufacturing lines

Simulation has been used to evaluate various aspects of manufacturing systems. However, building a simulation model of a manufacturing system is time-consuming and error-prone because of the complexity of the systems. This paper introduces a generic simulation modeling framework to reduce the simulation model build time. The framework consists of layout modeling software and a data-driven generic simulation model. The generic simulation model was developed considering the processing as well as the logistics aspects of assembly manufacturing systems. The framework can be used to quickly develop an integrated simulation model of the production schedule, operation processes and logistics of a system. The framework was validated by developing simulation models of cellular and conveyor manufacturing systems.     

Nanotextured superhydrophobic micromesh

A superhydrophobic micromesh covered with nanoprotrusions has been introduced and its applicability to a waterproof mobile phone speaker has been evaluated. The nanotextured superhydrophobic micromesh showed excellent water repellency, self-cleaning and waterproofing performances. In a waterproof speaker test using the fabricated nanotextured micromesh, the micromesh did not lose its waterproof function at 2 m water depth and did not form a remnant water film after being removed from the water. The packaged speaker showed almost the same sound quality before and after dipping at a 2 m water depth. These results demonstrate that the superhydrophobic nanotextured micromesh could be directly applicable for various products that need to resist water penetration, yet allow the transmission of gases and sound/light waves.     

A fact-oriented ontological approach to human process modeling for knowledge-intensive business services

Modern business environment emphasizes the role of knowledge-intensive business services (KIBS). As well as the enterprises in any other kinds of industries, business process management (BPM) can be a source of core competency for the enterprises in KIBS industry. However, most of the business processes in KIBS are human processes which are collaborative, innovative, and dynamic, which cannot be supported well by current BPM technologies. Human interaction management (HIM) has emerged as an alternative theory to deal with human processes in KIBS. But, the current ways of process modeling and management in HIM have a problem of complexity, especially in terms of realizing its principle of ‘supportive activity management’. This research adopts fact-oriented ontological approach to address this problem. Based on fact-oriented approach, human-friendly condition modeling and state management during supportive activity management can be achieved.     

Feature detection of triangular meshes based on tensor voting theory

This paper presents n-dimensional feature recognition of triangular meshes that can handle both geometric properties and additional attributes such as color information of a physical object. Our method is based on a tensor voting technique for classifying features and integrates a clustering and region growing methodology for segmenting a mesh into sub-patches. We classify a feature into a corner, a sharp edge and a face. Then, finally we detect features via region merging and cleaning processes. Our feature detection shows good performance with efficiency for various dimensional models.