A highly dependable self-adaptive mixed-signal multi-core system-on-chip architecture

In this paper we propose a design of a dependable self-organizing and adaptive mixed-signal SoC. We introduce an Artificial Hormone System (AHS) as a general control mechanism, which addresses the goals of organic computing methodology. Regarding the coexistence of digital and analog components in SoCs, we developed two new AHS implementations, one pure analog approach and one mixed-signal approach. Besides the basics of the hormone based control mechanisms, especially for the analog domain, this paper adapts the AHS upon mixed-signal SoC and presents the evaluation of a completely simulated AHS-controlled SoC. This evaluation verifies the approaches including stability issues as well as upper timing bounds and shows the improvement achieved on the system reliability. We also state the advantages from the hormone system compared to other approaches, as well as the strong points of the different hormone systems to one another.     

用土力学的新概念解读和修正经典饱和土力学

论述了应该用有效应力的新概念代替经典有效应力原理。总应力压缩模量与渗流水等效压缩模量的曲线相似;有效应力压缩模量起加劲作用,而不是起决定性作用。土的压缩变形计算中应用有效应力原理是错误的。对于起始水力坡降,引入饱和粘性土的自由水压力衰减概念。在饱和粘性土中,自由水通道率折减不大,相对自由水压力通过一定厚度后衰减为零。所以,新概念土力学的算法接近我国规范:"对粘性土宜按水土合算计算"。还解读了地下室浮力折减、流沙陷阱等等。     

Hyperspectral image compression based on lapped transform and Tucker decomposition

In this paper, we present a hyperspectral image compression system based on the lapped transform and Tucker decomposition (LT-TD). In the proposed method, each band of a hyperspectral image is first decorrelated by a lapped transform. The transformed coefficients of different frequencies are rearranged into three-dimensional (3D) wavelet sub-band structures. The 3D sub-bands are viewed as third-order tensors. Then they are decomposed by Tucker decomposition into a core tensor and three factor matrices. The core tensor preserves most of the energy of the original tensor, and it is encoded using a bit-plane coding algorithm into bit-streams. Comparison experiments have been performed and provided, as well as an analysis regarding the contributing factors for the compression performance, such as the rank of the core tensor and quantization of the factor matrices.     

Empirical examination of critical failure factors of continuous improvement deployments: stage-wise results and a contingency theory perspective

Organisations implement various Continuous Improvement (CI) practices such as Total Quality Management, Lean, Six Sigma and Lean Six Sigma (LSS) for improving their processes. Drawing from the success and failure stories of these structured CI practices, scholars enumerated Critical Success Factors and Critical Failure Factors (CFFs). This study empirically examines the occurrence of various CFFs across different stages of CI deployment. Further, from a contingency theoretic perspective, this study investigates their associations with contextual variables by collecting survey data from 213 business units from the USA, the UK, China, and India. Principal Component Analysis is used to group CFFs across five CI deployment stages leading to an empirically refined framework for CI. Crosstab analysis using the chi-square likelihood ratio presented associations of CFFs with contextual variables. Findings reveal significant differences in the occurrence of CFFs across countries. There is evidence that LSS is less prone to failures when compared with TQM, Lean and Six Sigma. The occurrence of CFFs has been relatively lower in smaller and medium-sized enterprises operating in the service sector. Findings also reveal that lessons learned from each CI deployment cycle, contribute to organisational learning, and thence, leading to success at the strategic CI level of maturity.     

316L板翅结构钎焊接头力学性能试验

采用高温短时拉伸试验和相应数学模型分析研究了316L T形钎焊接头的力学性能.使用扫描电镜（SEM）和能谱分析（EDS）分析了失效钎焊接头裂纹尖端和焊缝区域的微观组织及性能，进一步探讨了板翅钎焊结构的失效机理.结果表明，基于T形板试样的高温拉伸载荷位移曲线能较好地反映焊缝的起裂和裂纹扩展过程，1~2 mm/min为合理的试验加载速率取值范围；钎焊过程中焊缝区会出现明显的铁元素富集现象，为板翅结构高温失效的主要诱因.     

冲击位置对三明治型复合板材冲击性能的影响分析

釆用高强低伸工业涤纶为原料,加工织造了机织间隔织物,对间隔织物空间进行泡沫填充,面层与树脂进行复合,制成了三明治型复合材料板材。测试了冲击位置不同时复合板材耐冲击性能和冲击前后板材的侧压性能,分析了冲击位置对三明治型复合板材耐冲击性能的影响。结果表明,相同冲击能量作用下,冲击位置不同时,复合板材的表观破坏和冲击后侧压性能均有差异。在间隔织物规格相同的情况下,冲击位置在接结点处的板材表观冲击损伤大,但冲击发生在接结点处的板材受冲击后,所能承受的侧压载荷相对较大。     

Application of intensive hydrodynamic cavitation in liquids for synthesis of hydrogen and nanoparticles

In this paper, the results of experimental studies of hydrogen and nanoparticles production using intensive hydrodynamic cavitation in liquids are presented. Physicochemical processes occurring in a cavitation bubble at the last stage of its compression are very similar to processes occurring in the explosion chamber.The values of pressure and temperature achieved in this case ensure the thermodynamic stability of the reaction products and the production of a gaseous hydrogen and nanoclusters as a result of decomposition of molecules of liquid, which is confirmed by theoretical calculations.The controlled addition of hydrogen-containing liquids and the change in the compression conditions of cavitation bubbles make it possible to control the process of hydrogen synthesis, which is an important step in the development of modern high-tech alternative energy methods.The pulsation of a spherical cavity is described by the Kirkwood – Bethe equations, which are one of the most accurate mathematical models of pulsation processes at an arbitrary velocity of the cavity boundary. The model allows to describe the process of pulsations of cavitation cavities, conduct comprehensive parametric studies and evaluate the effect of various process parameters on the collapse of cavities.This work continues with the experiments on cavitation synthesis of carbon nanostructures. With the rapid movement of chemically pure hydrocarbons along the profiled channel in the form of a Venturi nozzle, cavitation bubbles form in the liquid, which are then compressed in the working chamber, in which a sharp pressure surge is created. The pressure in the shock wave, which reaches 80–90 MPa, ensures the collapse of cavitation bubbles close to adiabatic compression. As a result of the number of rapidly occurring physicochemical processes of evaporation, heating, and thermal dissociation of hydrocarbon vapors, a solid carbon phase including graphene oxide nanoparticles and a gaseous hydrogen-containing phase are synthesized in the cavitation, which is then subjected to separation. Synthesized graphene oxide nanoparticles possess activated surface due to the cavitation action and can be subsequently used as substrates for modification with functional nanoparticles, e.g. silver nanoparticles with antibacterial properties.The article is of great help to scientists and design engineers who are engaged in the development of promising hydrogen generating facilities and hydrogen complexes.     

STAR-Lite: A light-weight scalable self-taught learning framework for older adults’ activity recognition

Recognizing activities for older adults is challenging as we observe a variety of activity patterns caused due to aging (e.g., limited dexterity, limb control, slower response time) or/and underlying health conditions (e.g., dementia). However, existing literature with deep learning methods has successfully recognized activities when the dataset contains high-quality annotations and is captured in a controlled environment. On the contrary, data captured in a real-world environment, especially with older adults exhibiting memory-related symptoms, varying psychological and mental health status, reliance on caregivers to perform daily activities, and unavailability of domain-specific annotators, makes obtaining quality data with annotations challenging; leaving us with limited labeled data and abundant unlabeled data. In this paper, we hypothesize that projecting the labeled data representations comprising a specific set of activities onto a new representation space characterized by the unlabeled data comprising activities beyond the limited activities in the labeled dataset would help us rely less on the annotated data to improve activity detection performance. Motivated by this, we propose STAR-Lite, a self-taught learning framework that involves a pre-training framework to prepare the new representation space considering activities beyond the initial labels in the labeled dataset. STAR-Lite projects the labeled data representations on the new representation space characterized by unlabeled data labels and learns higher-level representations of the labeled dataset while optimizing inter- and intra- class distances without explicitly using a computation hungry similarity-based approach. We demonstrate that our proposed approach, STAR-Lite (a) improves activity recognition performance in a supervised setting and (b) is feasible for real-world deployment. To enhance the feasibility of deploying STAR-Lite on devices with limited memory resources, we explore model compression techniques such as pruning and quantization and propose a novel layer-wise pruning-rate optimization technique that effectively compresses the network while preserving the model performance. The evaluation was performed using the Alzheimer’s Activity Recognition dataset (AAR) captured from 25 individuals living in a retirement community center with IRB approval (#Y18NR12035) using an in-house SenseBox infrastructure while concurrently assessing the clinical evaluation of the participants for dementia, and independent living. Our extensive evaluation reveals that STAR-Lite can detect activities with an F1-score of 85.12% despite 62% reduction in model size and 5% improvement of execution time on a resource constrained device.     

An efficient method for estimating time-dependent failure possibility by combining adaptive Kriging with adaptive truncated fuzzy simulation

Time-dependent failure possibility (TDFP) can measure the structural safety level for a time interval of interest under fuzzy uncertainty, but its calculational cost is unaffordable by using fuzzy simulation (FS) due to a required large size of FS candidate sampling pool (CSP). Although time-dependent adaptive Kriging model (T-AK) combined with FS (T-AK-FS) was presented to reduce the number of calling performance function, a large FS CSP still makes training T-AK time-consuming. To improve its efficiency, an adaptive truncated FS (ATFS) with T-AK (T-AK-ATFS) is proposed by CSP size reduction approach. By T-AK-ATFS, the largest safety hypercube in fuzzy standard space is adaptively searched, in which the samples are in safety states and can be removed from the FS CSP. Moreover, T-AK is adaptively trained to search the largest safety hypercube and estimate TDFP simultaneously. In adaptively searching process, the FS CSP is divided into several sub-CSPs, on which training T-AK is more time-saving. Overall, strategies of T-AK-ATFS include proposing ATFS to reduce the FS CSP, adaptively searching the largest safety hypercube, estimating the TDFP with the same T-AK and training T-AK in the sub-CSPs sequentially. Verified by examples, these strategies make T-AK-ATFS more efficient than existing FS and T-AK-FS.