Customer entrepreneurship on digital platforms: Challenges and solutions for platform business models

Drawing on the mixed methods of qualitative research and agent‐based simulation, this study examines: (a) how end‐users use digital platforms to become customer–entrepreneurs undertaking commercial activities on platforms; and (b) how platform providers can convert this customer entrepreneurship into a revenue stream. Considering that end‐users have traditionally been defined as passive and uncharged actors in platform business models, an in‐depth understanding of their commercial activities and the viable revenue model to monetize this emerging customer practice is warranted. Our qualitative study reveals that customer–entrepreneurs make substantial use of platform offerings to advertise their products; communicate with end‐consumers; and accept payments. These commercial activities are largely exercised for free on platforms, even though they could otherwise serve as a source of revenue. On this point, our simulation results identify two pricing models achieving the generation of nearly identical revenues over time. First, platform providers may charge both advertising and transaction fees, which maximize the survival of professional customer–entrepreneurs. Second, platform businesses may levy advertising fees only, which maximizes the survival of informal customer–entrepreneurs operating on a micro‐scale and part‐time basis. This study offers theoretical, methodological, and managerial implications for platform studies.     

Adaptive synthesis of distance fields

We address the computational resource requirements of 3D example-based synthesis with an adaptive synthesis technique that uses a tree-based synthesis map. A signed-distance field (SDF) is determined for the 3D exemplars, and then new models can be synthesized as SDFs by neighborhood matching. Unlike voxel synthesis approach, our input is posed in the real domain to preserve maximum detail. In comparison to straightforward extensions to the existing volume texture synthesis approach, we made several improvements in terms of memory requirements, computation times, and synthesis quality. The inherent parallelism in this method makes it suitable for a multicore CPU. Results show that computation times and memory requirements are very much reduced, and large synthesized scenes exhibit fine details which mimic the exemplars.     

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.     

Implementation of MAC-based RTL module for Inverse DCT in H.264/AVC

In this paper, we implemented the MAC-based RTL module for inverse DCT in H.264/AVC to improve applicability, to reduce processing time and utilize resources. The paper highlights design of FU architecture, its interconnection topology, regular formula of inverse DCT and array processor mapping as well as MAC-based RTL module constructing. Multi-directional FUA and FPGA were presented along with an evaluated performance and simulation result. Hence, the paper encompasses design of single FU that was verified with the performance test at maximum frequency 200 MHz; the designed 4-by-4 FUA operates over 100 MHz. The proposed multi-directional FU can be extended to n-by-n FUA that functionality can be extended to next video coding standard (H.265/HEVC).     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.     

Cascade filter structure for sensor/actuator fault detection and isolation of satellite attitude control system

This paper presents a new scheme for fault detection and isolation in a satellite system. The purpose of this paper is to develop detection, isolation and identification algorithms based on a cascade filter for both total and partial faults in a satellite attitude control system (ACS). The cascade filter consists of a decentralized Kalman filter (DKF) and a bank of interacting multiple model (IMM) filters. The cascade filter is utilized for detection and diagnosis of anticipated sensor and actuator faults in a satellite ACS. Other fault detection and isolation (FDI) schemes are compared with the proposed FDI scheme. The FDI procedure using a cascade filter was developed in three stages. In the first stage, two local filters and a master filter detect sensor faults. In the second stage, the FDI scheme checks sensor residuals to isolate sensor faults, and 11 Extended Kalman filters with actuator fault models detect wherever actuator faults occur. In the third stage of the FDI scheme, four filters identify the fault type, which is either a total or partial fault. An important feature of the proposed FDI scheme is that it can decrease fault isolation time and accomplish not only fault detection and isolation but also fault type identification using a scalar penalty in the conditional density function.     

Modeling and controlling the mobile harbour crane system with virtual prototyping technology

Virtual simulation of the real behaviour of mobile harbour crane (MHC) without using the traditional build-and-test method is an imperative approach to the design stage that can increase the quality of the product by reducing manufacturing cost and errors. This paper introduces an engineering model that describes the mechanical behaviour of MHC, and the control design for increasing the position accuracy. Based on a concept of the MHC, a virtual mechanical model was created using SOLIDWOKS, which was then exported to the Automatic Dynamic Analysis of Mechanical System (ADAMS) environment. This model was simulated to investigate the dynamic behaviour of the MHC system. In addition, an adaptive siding mode PID controller was also developed in MATLAB/Simulink to control the crane trolley position and suppress the swing angle of the load. This co-simulation demonstrates the reliability of the mechanical and control functionalities of the developed system.     

The α- to β-Si3N4 transformation in the presence of liquid silicon

The compacts consisted of , -Si₃N₄ and free silicon are heat treated in the range 1650° C to 1750° C in an argon atmosphere in order to observe the following behaviours; the to phase transformation and variations of the microstructure during heat treatment in silicon nitride. For the microstructural observation of the heat treated specimens, the same grains in the polished surface were investigated before and after eliminating the retained silicon by etching. The to phase transformation, in this case, occurs via silicon melts irrespective of added -Si₃N₄. Both and phases are soluted and precipitated into molten silicon and their morphology are changed from an equiaxed shape to prismatic one. Although elongated grains are precipitated at low temperature or in the early stage of heat treatment, fine precipitated grains are mainly observed with increasing heat treating temperature.     

Dielectric properties and microstructural behaviour of B-site calcium-doped barium titanate ceramics

Dielectric properties and microstructural behaviour of Ba_1–x Sr _x Ti_1–y Ca _y O_3–y ceramics, where strontium and calcium were doped on the barium and titanium sites, respectively, within the range 0x0.24 and 0y0.05, were investigated. Calcium addition decreased the tetragonality,c/a, increased the unit cell volume, and lowered the Curie temperature, which were all attributed to the occupancy of Ca²⁺ ions on titanium sites. When sintered at a low oxygen partial pressure of 10^–9 MPa, a resistivity higher than 10¹¹ cm was maintained for the formulations containing B-site calcium substitution more than 0.5 mol %. With increasing the amount of calcium addition, the Curie peak was depressed and completely broadened for the compositions with calcium addition more than 3 mol %, where the average grain size was smaller than 1 m. Co-firing with nickel electrodes in a reducing atmosphere also depressed the Curie peak and inhibited the grain growth due to the diffusion of nickel into the dielectrics.     

利用CFD技术研究四边形通道内的流场

了解认知隧道内的流体力学的特点对于隧道火灾的研究和隧道安全建设有着非常大的意义。为了对隧道火灾的特点进行研究,我们使用商用CFD程序STAR-CD对速度场和压力分布进行了数值模拟。模拟过程中在同一入口位置的情况下改变四个出口位置,出口位置与模具高度的数值对别分别为L/H=0,0.375,0.75,1.125。同时还介绍了对于火灾救援和人员疏散的有效措施。