Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation

EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System

IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources. However, the power provided by the energy harvester is low and has an intrinsic drawback of instability since it varies with the ambient environment. This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Those shared models incur significantly reduced memory footprint with different levels of model sparsity, latency, and accuracy to adapt to the environmental changes. An efficient on-device implementation architecture is further developed to efficiently execute each model on device. A run-time model extraction algorithm is proposed that retrieves individual model with negligible overhead when a specific model mode is triggered. Experimental results show that the neural networks models generated by EVE is on average 2.5X times faster than the baseline models without pruning and shared weights

Hardware Security Challenges Beyond Cmos: Attacks And Remedies

The globalization of Integrated Circuits (ICs) supply chain has raised security concerns on how to ensure the integrity and the trustworthiness of fabricated circuits. While existing attack and protection methods are developed for CMOS based circuits, the introduction of emerging transistors acts as a double-sided sword. The usage of emerging devices introduces new security issues which the attackers can leverage to launch hardware attacks. On the other hand, the unique properties of emerging devices also provides a great opportunity for defenders to develop innovative hardware security primitives and to construct resilient hardware platforms for cybersecurity. In this paper, we will summarize the previous work in both directions, attacks and remedies with a focus on the authors\u27 previous work in this domain. We will also discuss the research trends so that the emerging devices can better help secure our computing systems, besides their roles in extending the Moore\u27s Law

A Statistical Stt-Ram Retention Model For Fast Memory Subsystem Designs

Spin-transfer torque random access memory (STT-RAM) is a promising nonvolatile memory (NVM) solution to implement on-chip caches and off-chip main memories for its high integration density and short access time, but it suffers from considerable write latency and energy overhead. Aggressively relaxing its non-volatility for write fast and write energy efficient memory subsystems has been quite debatable, due to the unclear retention behavior on a timescale of microseconds-to-seconds. Moreover, recent studies project that retention failure will eventually dominate the cell reliability as STT-RAM scales. As a result, a comprehensive understanding of the thermal noise induced STT-RAM retention mechanism has become a must. In this work, we develop a compact semi-analytical model for fast retention failure analysis. We then systematically analyze critical factors (e.g., initial angle, device dimension etc.) and their impacts on the STT-RAM retention behavior through our model. Our experimental results show that STT-RAM suffers from a soft-error style retention failure, which may happen instantly just after the last write finishes and is totally different from that of DRAM and Flash, i.e., the gradual charge loss process. Our model offers an excellent agreement with the results from golden macro-magnetic simulations in the region of interest without conducting expensive Monte-Carlo runs. At last, we demonstrate our model can enable architectural designers to rethink STT-RAM based memory designs by emphasizing its probabilistic retention property

Edge detection guide network for semantic segmentation of remote-sensing images

The acquisition of high-resolution satellite and airborne remote sensing images has been significantly simplified due to the rapid development of sensor technology. Several practical applications of high-resolution remote sensing images (HRRSIs) are based on semantic segmentation. However, single-modal HRRSIs are difficult to classify accurately in the complex situation of some scene objects; therefore, the semantic segmentation of multi-source information fusion is gaining popularity. The inherent difference between multimodal features and the semantic gap between multi-level features typically affect the performance of existing multi-mode fusion methods. We propose a multimodal fusion network based on edge detection to address these issues. This method aids multimodal information fusion by utilizing spatial information contained in the boundary. An edge detection guide module is included in the feature extraction stage to realize the boundary information through the fusion of details and semantics between high-level and low-level features. The boundary information is extended into the well-designed multimodal adaptive fusion block (MAFB) to obtain the multimodal fusion features. Furthermore, a residual adaptive fusion block (RAFB) and a spatial position module (SPM) in the feature decoding stage have been designed to fuse multi-level features from the standpoint of local and global dependence. We compared our method to several state-of-the-art (SOTA) methods using the International Society for Photogrammetry and Remote Sensing's (ISPRS) Vaihingen and Potsdam datasets. The final results demonstrate that our method achieves excellent performance.This work was supported by the National Natural Science Foundation of China under Grant 61502429

Autoencoder-Like Knowledge Distillation Network for Anomaly Detection

Anomaly detection is a crucial research field in computer vision with diverse applications in practical scenarios. The common anomaly detection methods employed currently consist of autoencoders, generative adversarial networks, and knowledge distillation (KD) models. However, the teacher and student models in KD might not always yield distinct representations to signify anomalies due to their similar model structure and data flow. This study proposes a novel autoencoder-like KD model based on the attention mechanism for anomaly detection. The pre-trained teacher model incorporates a dual attention module as the encoder, while the student model integrates the same dual attention module as the decoder. The teacher guides the student to learn the feature knowledge of the input image. To connect the teacher-student model, a BottleNeck module is employed, converting the features extracted from the teacher model into more compact latent codes for precise restoration by the student model, thereby achieving anomaly detection. In general, the proposed model exhibits superior performance compared to other existing anomaly detection models on specific datasets. Experimental results demonstrate that the proposed model attains the state-of-the-art (SOTA) performance in anomaly detection on the public dataset MVTec. It achieves an average AUC of 98.2% and 98.0% at sample and pixel levels, respectively

DASFNet: Dense-Attention–Similarity-Fusion Network for scene classification of dual-modal remote-sensing images

Although significant progress has been made in scene classification of high-resolution remote-sensing images (HRRSIs), dual-modal HRRSI scene classification is still an active and challenging issue. In this study, we introduce an end-to-end dense-attention–similarity-fusion network (DASFNet) for dual-modal HRRSIs. Specifically, we propose a dense-attention map module based on graph convolution, which adaptively captures long-range semantic cues and further directs shallow-attention cues to the deep level to guide the generation of high-level feature attention cues. At the encoder stage, DASFNet uses feature similarity to explore the correlation between dual-modal features; a similarity-fusion module extracts complementary information by fusing features from different modalities. A multiscale context-feature-aggregation module is used to strengthen the feature embedding of any two spatial scales; this solves the of scale change problem. A large number of experiments on two HRRSI benchmark datasets for scene classification indicate that the proposed DASFNet outperforms the outstanding scene classification approaches