Efficiency and Returns to Scale Measurements with Shared Inputs in Multi-Activity Data Envelopment Analysis: An Application to Farmers' Organizations in Taiwan

This paper addresses the question how team production promotes efficiency of a firm when some inputs can be rewarded on the basis of outputs but some cannot because they are shared among outputs and non-separable. A multi-activity DEA model with variable returns to scale is proposed to provide information on the efficiency performance for organizations with inputs shared among several closely related activities. The model is applied to study the case of 279 farmers' associations in Taiwan. The result suggests that it is important to improve the efficiency of the non-profit oriented activities to improve their overall performances. Three out of four departments of TFAs can gain from economies of scale through expansion, while the remaining one gains through contraction. Thus, policies promoting structural adjustment and consolidations of TFAs would not be inconsistent with public interests.multi-activity DEA, shared inputs, efficiency measure, directional distance function, Productivity Analysis,

Multi-Agent Orbit Design For Perception Enhancement Purpose

This paper develops a robust optimization based method to design orbits on which the sensory perception of the desired physical quantities are maximized. It also demonstrates how to incorporate various constraints imposed by many spacecraft missions such as collision avoidance, co-orbital configuration, altitude and frozen orbit constraints along with Sun-Synchronous orbit. The paper specifically investigates designing orbits for constrained visual sensor planning applications as the case study. For this purpose, the key elements to form an image in such vision systems are considered and effective factors are taken into account to define a metric for perception quality. The simulation results confirm the effectiveness of the proposed method for several scenarios on low and medium Earth orbits as well as a challenging Space-Based Space Surveillance program application.Comment: 12 pages, 18 figure

Quantitative Assessment of Flame Stability Through Image Processing and Spectral Analysis

This paper experimentally investigates two generalized methods, i.e., a simple universal index and oscillation frequency, for the quantitative assessment of flame stability at fossil-fuel-fired furnaces. The index is proposed to assess the stability of flame in terms of its color, geometry, and luminance. It is designed by combining up to seven characteristic parameters extracted from flame images. The oscillation frequency is derived from the spectral analysis of flame radiation signals. The measurements involved in these two methods do not require prior knowledge about fuel property, burner type, and other operation conditions. They can therefore be easily applied to flame stability assessment without costly and complex adaption. Experiments were carried out on a 9-MW heavy-oil-fired combustion test rig over a wide range of combustion conditions including variations in swirl vane position of the tertiary air, swirl vane position of the secondary air, and the ratio of the primary air to the total air. The impact of these burner parameters on the stability of heavy oil flames is investigated by using the index and oscillation frequency proposed. The experimental results obtained demonstrate the effectiveness of the methods and the importance of maintaining a stable flame for reduced NOx emissions. It is envisaged that such methods can be easily transferred to existing flame closed-circuit television systems and flame failure detectors in power stations for flame stability monitoring

The Effect of Mo Doping on The Charge Separation Dynamics and Photocurrent Performance of BiVO\u3csub\u3e4\u3c/sub\u3e Photoanodes

Doping with electron-rich elements in BiVO4 photoanodes has been demonstrated as a desirable approach for improving their carrier mobility and charge separation efficiency. However, the effect of doping and dopant concentration on the carrier dynamics and photoelectrochemical performance remains unclear. In this work, we examined the effects of Mo doping on the charge separation dynamics and photocurrent performance in BiVO4photoanodes. We show that the photocurrent of BiVO4 photoanodes increases with increasing concentration of the Mo dopant, which can be attributed to both the improved carrier mobility resulting from increased electron density and charge separation efficiency due to the diminishing of trap states upon Mo doping. The effect of doping on the electronic structure, carrier dynamics and photocurrent performance of BiVO4 photoanodes resulting from W and Mo dopants was also compared and discussed in this study. The knowledge gained from this work will provide important insights into the optimization of the carrier mobility and charge separation efficiency of BiVO4 photoanodes by controlling the dopants and their concentrations

Balancing clusters to reduce response time variability in large scale image search

Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, in order to avoid exhaustive search, an index selects the few (or a single) clusters nearest to the query point. Clusters are often produced by the well-known $k$-means approach since it has several desirable properties. On the downside, it tends to produce clusters having quite different cardinalities. Imbalanced clusters negatively impact both the variance and the expectation of query response times. This paper proposes to modify $k$-means centroids to produce clusters with more comparable sizes without sacrificing the desirable properties. Experiments with a large scale collection of image descriptors show that our algorithm significantly reduces the variance of response times without seriously impacting the search quality

Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17