Color image segmentation using tensor voting based color clustering

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.     

A new seam-tracking algorithm through characteristic-point detection for a portable welding robot

The welding task in double-hulled structures in shipyards and in steel-frame structures is hazardous and difficult due to the toxic gas and limited workspace. Therefore, many efforts have been undertaken for automation. The main challenge for automation is the development of a simple and robust seam-tracking algorithm that can be applied to a portable welding robot that operates under irregular and diverse task conditions in the workspace. We developed a seam-tracking algorithm for weaving weld path planning using a laser displacement sensor. The goal of the proposed algorithm is to detect the seam of single-butt welding with manually tack-welded non-zero gaps. The focus is on keeping the algorithm simple and affordable so that it can be applied to portable robots that operate in hazardous fields. The algorithm consists of four steps: scanning, filtering, generation of the reference points, and path planning. In the scanning process, the depth data of a cross-section of the seam profile is obtained. Next, a Gaussian filter is used to remove noise from the raw data. A differential characteristic-point detection algorithm is applied to the filtered data to detect the reference points that represent the shape and location of the gap to be welded. Finally, path planning for single-V butt multi-pass welding is done based on the detected reference points. A portable four-axis welding robot is built using the developed algorithm. The algorithm is validated through welding experiments regarding a single-V butt welding task with a manually tack-welded non-zero gap.     

Molybdenum and Phosphorous Dual Doping in Cobalt Monolayer Interfacial Assembled Cobalt Nanowires for Efficient Overall Water Splitting

The necessity for better water splitting requires speedy development of efficient catalysts with high activity, long‐term stability, and cost effectiveness. In this work, a bifunctional catalyst originating from the interfacial assembly of a thin Mo,P‐codoped Co layer (≈50 nm) shelled Co nanowire (Co‐Mo‐P/CoNWs) network is fabricated via a facile approach. The catalyst exhibits low overpotentials of 0.08 and 0.27 V to reach a current response of 20 mA cm^?2 for the hydrogen evolution reaction and oxygen evolution reaction, respectively, together with long‐term stability in 1.0 m KOH medium. The outstanding performance is further demonstrated by a Co‐Mo‐P/CoNWs‐based electrolyzer, which enables a cell voltage of only 1.495 V to reach 10 mA cm^?2, superior to one derived from commercial (Pt/C + RuO₂/C) as well as to various reports recently published elsewhere. It is recognized that the formation of multiactive centers together with the increased active site number caused by Mo and P dual doping synergistically promote both hydrogen and oxygen evolution performance. Such a hybrid material opens a new approach for developing efficient and cost‐effective catalysts for water splitting application.     

Gait planning based on kinematics for a quadruped gecko model with redundancy

Recent research on mobile robots has focused on locomotion in various environments. In this paper, a gait-generation algorithm for a mobile robot that can travel from the ground to a wall and climb vertical surfaces is proposed. The algorithm was inspired by a gecko lizard. Our gait planning was based on inverse kinematics using the Jacobian of the whole body, where the redundancy was solved by defining an object function for the gecko posture to avoid collisions with the surface. The optimal scalar factor for these two objects was obtained by defining a superior object function to minimize the angular acceleration of joints. The algorithm was verified through simulation of the gecko model travelling on given task paths and avoiding abnormal joint movements and collisions.     

A wireless powered fully integrated SU-8-based implantable LC transponder

In this work, we report a SU-8-based fully integrated miniaturized inductively powered LC transponder for generic implantable wireless sensor applications. It consists of a 1 mm diameter octagonal spiral inductor and a micro fabricated MIM (metal insulator metal) capacitor. Polyvinylidene Fluoride–Trifluoroethylene (PVDF–TrFE) copolymer is applied as a dielectric material for the capacitor fabrication due to its high dielectric constant. The 1 mm diameter, 154 nH spiral inductor is built on top of the capacitor. The capacitor and the inductor are in parallel connection through SU-8 via holes. SU-8 is used as a packaging material due to its biocompatibility, and also it serves as an insulator between the capacitor and the spiral inductor. The operating frequencies of the LC tanks are decided by the sizes of the capacitors (45 × 45, 55 × 55, 95 × 95 and 100 × 100 μm), and measured operating frequency range is from 385 to 485 MHz. The fabricated LC tanks are held to the power transmitting coil coaxially at distances of 2, 5, 7 and 10 mm, and rectified induced voltage at the LC tank is 8.5 V with 29 dBm input power at a 5 mm distance.     

Hybrid agent-based simulation for policy evaluation of solar power generation systems

To encourage the adoption of solar power as well as new technological improvements in solar industry, state and federal governments have employed various kinds of incentives over the past decades, such as rebates, tax return opportunities, and Net Metering credits. At the same time, however, the governments concern regulations to avoid highly steep growth of solar energy without considering necessary supporting structure such as storage components, which will increase the electricity price and threaten the stability of existing transmission systems. The goal of this research is to develop a decision support tool to analyze the effectiveness of various policies (both incentives as well as regulations) on the proper growth rate of distributed photovoltaic (PV) systems avoiding the instability of the transition system or steep rising of the electricity price. To this end, we propose a hybrid two-level simulation modeling framework, which is significantly more detailed than the simplified structures commonly used in most policy evaluations. The lower-level model concerns the calculation of PV system payback period of individual household based on hourly electricity generation (PV) and consumptions, incentive levels, PV module price, and hourly electricity price (grid). The higher-level model, running on a weekly basis for 20 years, concerns the household adoption behaviors of the PV systems influenced by various factors, including payback period, household income, word-of-mouth effect and advertisement effect. Agent-based and system dynamics modeling techniques are leveraged in both levels. The proposed models have been developed for residential areas at two different regions in the US based on real data, which have been used to illustrate the impact of policies in different regions.     

Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta,Vietnam

Estimating the area of rice planting is vital for production prediction. This study utilizes time-series MODIS NDVI data from 2002 to 2007 to discriminate rice cropping systems in the Mekong Delta (MD), Vietnam. Data are processed using Empirical Mode Decomposition (EMD) and the Linear Mixture Model (LMM). Various spatial and non-spatial data are also collected for accuracy validation. The results indicate that EMD acts as a well-fitted filter for noise reduction of the time-series NDVI data. The classification results derived from the LMM for 2002 showed an overall classification accuracy of 71.6% and a Kappa coefficient of 0.6. The provincial level area estimates were strongly correlated with the rice statistics. An examination of the change in cropping patterns between 2002 and 2007 showed that 29.0% of the triple irrigated-rice cropping systems had been changed to double irrigated-rice cropping systems and that 12.0% and 9.0% of the double irrigated and rainfed-rice cropping systems, respectively, had been changed to triple rice cropping systems. These changes were verified by visual comparisons with Landsat images.     

Robust interval observers and stabilization design for discrete-time systems with input and output

For a family of nonlinear discrete-time systems with input, output and uncertain terms, a new interval observer is designed. Its main feature is that it is composed of two copies of classical observers. This interval observer applies in the presence of unknown bounded nonlinear terms and additive disturbances and is used to achieve asymptotic stability through an appropriate choice of dynamic output feedback. An illustrative example completes the presentation.     

System sourcing and information processing capability in supply chains: a study of small suppliers

Although the adoption of inter-organizational systems (IOS) has been studied extensively in the existing literature, relatively little research attention has been paid to how small firms in supply chains are influenced by their larger partners in making their system sourcing decisions. In this study, we examine how small suppliers may choose their system sourcing approaches to better serve and collaborate with their large customers in supply chains. Our empirical findings suggest that information processing requirements and collaboration needs drive small suppliers to adopt more commercial off-the-shelf software package (i.e., external sourcing) to build their IOSs. Moreover, we find that small suppliers take such external sourcing as a way to achieve system effectiveness and enhance their information processing capabilities. The findings of this study generate important theoretical and managerial implications for IOS adoption and organizational information processing.     

Picture fuzzy clustering: a new computational intelligence method

Fuzzy clustering especially fuzzy \(C\)-means (FCM) is considered as a useful tool in the processes of pattern recognition and knowledge discovery from a database; thus being applied to various crucial, socioeconomic applications. Nevertheless, the clustering quality of FCM is not high since this algorithm is deployed on the basis of the traditional fuzzy sets, which have some limitations in the membership representation, the determination of hesitancy and the vagueness of prototype parameters. Various improvement versions of FCM on some extensions of the traditional fuzzy sets have been proposed to tackle with those limitations. In this paper, we consider another improvement of FCM on the picture fuzzy sets, which is a generalization of the traditional fuzzy sets and the intuitionistic fuzzy sets, and present a novel picture fuzzy clustering algorithm, the so-called FC-PFS. A numerical example on the IRIS dataset is conducted to illustrate the activities of the proposed algorithm. The experimental results on various benchmark datasets of UCI Machine Learning Repository under different scenarios of parameters of the algorithm reveal that FC-PFS has better clustering quality than some relevant clustering algorithms such as FCM, IFCM, KFCM and KIFCM.