Design and kinematic analysis of new multi-mode mobile parallel mechanism with deployable platform

Purpose This study aims to make the mobile robot better adapt to the patrol and monitoring in industrial field substation area, a multi-mode mobile carrying mechanism which can carrying data collector, camera and other equipment is designed. Design/methodology/approach Based on the geometric axis analysis and interference analysis, the multi-mode mobile carrying mechanism is designed. The screw constraint topological theory and zero-moment point (ZMP) theory is used to kinematic analysis in mechanism mobile process. Findings The mobile carrying mechanism can realize the folding movement, hexagonal rolling and quadrilateral rolling movement. A series of simulation and prototype experiment results verify the feasibility and actual error of the design analysis. Originality/value The work of this paper provides a mobile carrying mechanism for carrying different data acquisition equipment and surveillance camera in industrial field substation zone. It has excellent folding performance and mobile capabilities. The mobile carrying mechanism reduces the workload of human being and injuries suffered by workers in industrial substation area.

Analysis of the folded normal distribution of a random variable

Анализ поведения случайных величин после различных преобразований можно применять при решении многих нетривиальных задач. В частности, решения, которые невозможно выразить аналитически, с точки зрения практической применимости способны давать результаты с точностью, достаточной для вычислений, вынося невыразимую невязку аналитического решения далеко за рамки требуемой погрешности. В настоящей работе исследовано поведение модуля нормально распределенной случайной величины и выяснено, при каких условиях можно пренебречь операцией взятия абсолютного значения и аппроксимировать модуль случайной величины {\it похожим} распределением вероятностей. The analysis of the behavior of random variables after various transformations can be used in the practical solution of many non-trivial problems. In particular, solutions that cannot be expressed purely analytically, from the point of view of practical applicability, are able to give results with accuracy sufficient for real calculations, taking the inexpressible discrepancy of the analytical solution far beyond the actual error. In this paper, the behavior of the modulus of a normally distributed random variable is investigated and it is found out under what conditions it is possible to neglect the operation of taking an absolute value and approximate the modulus of a random variable with a {\it similar} probability distribution.

Application of spatial auto-beta models in statistical classification

In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.

EfficientNet-B0 Based Monocular Dense-Depth Map Estimation

Monocular depth estimation is a hot research topic in autonomous car driving. Deep convolution neural networks (DCNN) comprising encoder and decoder with transfer learning are exploited in the proposed work for monocular depth map estimation of two-dimensional images. Extracted CNN features from initial stages are later upsampled using a sequence of Bilinear UpSampling and convolution layers to reconstruct the depth map. The encoder forms the feature extraction part, and the decoder forms the image reconstruction part. EfficientNetB0, a new architecture is used with pretrained weights as encoder. It is a revolutionary architecture with smaller model parameters yet achieving higher efficiencies than the architectures of state-of-the-art, pretrained networks. EfficientNet-B0 is compared with two other pretrained networks, the DenseNet-121 and ResNet50 models. Each of these three models are used in encoding stage for features extraction followed by bilinear method of UpSampling in the decoder. The Monocular image is an ill-posed problem and is thus considered as a regression problem. So the metrics used in the proposed work are F1-score, Jaccard score and Mean Actual Error (MAE) etc., between the original and the reconstructed image. The results convey that EfficientNet-B0 outperforms in validation loss, F1-score and Jaccard score compared to DenseNet-121 and ResNet-50 models.

Permchecker: a toolchain for debugging memory managers with typestate

Dynamic memory managers are a crucial component of almost every modern software system. In addition to implementing efficient allocation and reclamation, memory managers provide the essential abstraction of memory as distinct objects, which underpins the properties of memory safety and type safety. Bugs in memory managers, while not common, are extremely hard to diagnose and fix. One reason is that their implementations often involve tricky pointer calculations, raw memory manipulation, and complex memory state invariants. While these properties are often documented, they are not specified in any precise, machine-checkable form. A second reason is that memory manager bugs can break the client application in bizarre ways that do not immediately implicate the memory manager at all. A third reason is that existing tools for debugging memory errors, such as Memcheck, cannot help because they rely on correct allocation and deallocation information to work. In this paper we present Permchecker, a tool designed specifically to detect and diagnose bugs in memory managers. The key idea in Permchecker is to make the expected structure of the heap explicit by associating typestates with each piece of memory. Typestate captures elements of both type (e.g., page, block, or cell) and state (e.g., allocated, free, or forwarded). Memory manager developers annotate their implementation with information about the expected typestates of memory and how heap operations change those typestates. At runtime, our system tracks the typestates and ensures that each memory access is consistent with the expected typestates. This technique detects errors quickly, before they corrupt the application or the memory manager itself, and it often provides accurate information about the reason for the error. The implementation of Permchecker uses a combination of compile-time annotation and instrumentation, and dynamic binary instrumentation (DBI). Because the overhead of DBI is fairly high, Permchecker is suitable for a testing and debugging setting and not for deployment. It works on a wide variety of existing systems, including explicit malloc/free memory managers and garbage collectors, such as those found in JikesRVM and OpenJDK. Since bugs in these systems are not numerous, we developed a testing methodology in which we automatically inject bugs into the code using bug patterns derived from real bugs. This technique allows us to test Permchecker on hundreds or thousands of buggy variants of the code. We find that Permchecker effectively detects and localizes errors in the vast majority of cases; without it, these bugs result in strange, incorrect behaviors usually long after the actual error occurs.

A generalized Kalman filter with its precision in recursive form when the stochastic model is misspecified

In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.

Integrity investigation of global ionospheric TEC maps for high-precision positioning

Aside from the ionospheric total electron content (TEC) information, root-mean-square (RMS) maps are also provided as the standard deviations of the corresponding TEC errors in global ionospheric maps (GIMs). As the RMS maps are commonly used as the accuracy indicator of GIMs to optimize the stochastic model of precise point positioning algorithms, it is of crucial importance to investigate the reliability of RMS maps involved in GIMs of different Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS), i.e., the integrity of GIMs. We indirectly analyzed the reliability of RMS maps by comparing the actual error of the differential STEC (dSTEC) with the RMS of the dSTEC derived from the RMS maps. With this method, the integrity of seven rapid IGS GIMs (UQRG, CORG, JPRG, WHRG, EHRG, EMRG, and IGRG) and six final GIMs (UPCG, CODG, JPLG, WHUG, ESAG and IGSG) was examined under the maximum and minimum solar activity conditions as well as the geomagnetic storm period. The results reveal that the reliability of the RMS maps is significantly different for the GIMs from different IAACs. Among these GIMs, the values in the RMS maps of UQRG are large, which can be used as ionospheric protection level, while the RMS values in EHRG and ESAG are significantly lower than the realistic RMS. The rapid and final GIMs from CODE, JPL and WHU provide quite reasonable RMS maps. The bounding performance of RMS maps can be influenced by the location of the stations, while the influence of solar activity and the geomagnetic storm is not obvious.

Examining the equivalence between imagery and execution within the spatial domain – Does motor imagery account for signal-dependent noise?

Motor imagery is suggested to be functionally equivalent to physical execution as they each utilise a common neural representation. The present study examined whether motor imagery correspondingly reflects the spatial characteristics of physically executed movements, including the signal-dependent noise that typically manifests in more variable end locations (as indicated by effective target width; We). Participants executed or imagined a single, upper-limb target-directed aim in the horizontal medio-lateral direction. The start and end of the imagined movements were indexed by the lifting and lowering of the limb over the home position, respectively. Following each imagined movement, participants had to additionally estimate their imagined end location relative to the target. All the movements had to be completed at a pre-specified criterion time (400 ms, 600 ms, 800 ms). The results indicated that the We increased following a decrease in movement time for execution, but not imagery. Moreover, the total error of imagined movements was greater than the actual error of executed movements. While motor imagery may comprise a neural representation that also contributes to the execution of movements, it is unable to closely reflect the random sources of variability. This limitation of motor imagery may be attributed to the comparatively limited efferent motor signals.