Cloud-hosted large language models (LLMs) have become the de facto planners in agentic systems, coordinating tools and guiding execution over local environments. In many deployments, however, the environment being planned over is private, containing source code, files, credentials, and metadata that cannot be exposed to the cloud. Existing solutions address adjacent concerns, such as execution isolation, access control, or confidential inference, but they do not control what cloud planners observe during planning: within the permitted scope, raw environment state is still exposed. We introduce PLANTWIN, a schema-constrained projection based architecture for cloud-assisted planning that prevents raw local context from leaving the local boundary. The key idea is to project the real environment into a planning-oriented digital twin: a schema-constrained and de-identified abstract graph that preserves planning-relevant structure while removing reconstructable details. The cloud planner operates solely on this sanitized twin through a bounded capability interface, while a local gatekeeper enforces safety policies and cumulative disclo sure budgets. We further formalize the privacy–utility trade-off as a capability granularity problem, define architectural privacy goals using (k,δ)-anonymity and ε-unlinkability, and mitigate compositional leakage through multi-turn disclosure control. We implement PLANTWIN as middleware between local agents and cloud planners and evaluate it on 60 agentic tasks across ten domains with four cloud planners. PLANTWIN achieves SND = 1.0 against passive-observer adversaries, while maintaining planning quality close to full-context systems: three of four cloud planners achieve PQS > 0.79, within ∼4% of the no-privacy Raw Context baseline; the privacy-hardening pipeline stages add less than 2.2 percentage points of further PQS variation. Residual identifiability under stronger structural-fingerprint adversaries persists and is bounded by deployment-side controls rather than architecturally eliminated.

Correspondence matching is a fundamental and crucial task in robot vision. In recent years, deep learning-based keypoint matching techniques have shown outstanding performance in downstream tasks. Conventional learning-based correspondence matching methods rely on large datasets and a specific training procedure. Correspondence techniques based on pre-trained features have been preliminarily explored by researchers. Unfortunately, traditional convolutional neural networks only possess translation invariance but lack rotational invariance, hence, their performance suffers significantly under heavy rotations. Therefore, we propose a correspondence matching method based on pre-trained group-equivariant neural networks and compare the performance of various rotation-equivariant to rotation-invariant transformers. We conducted experiments on the Rotated-Hpatches and Rotated-MegaDepth datasets, and the results indicate that our proposed method is concise and effective, achieving state-of-the-art performance without the need for retraining in downstream tasks.

We propose a Visual Place Recognition (VPR) framework by sharing lightweight keypoint extraction modules for local features. Current research on the joint learning of local keypoint matching and VPR is relatively scarce, and the application deployment of real-time spatial computing on edge devices has a high learning cost. There is also a significant spatial structural difference between existing VPR methods and the scenarios in practical applications. To address these issues, we design a joint learning framework for local keypoint extraction and VPR, which shares local features and fuses irregularly distributed key features in space through self-attention and cross-attention mechanisms. Our framework achieves excellent results on several VPR datasets. In particular, we introduce a new VPR dataset, called TJPark, which has a significant spatial information difference from common street view data. Our method demonstrates that local features with strong generalization capabilities effectively help enhance the generalization of VPR. Our open source code and dataset are available at: https://github.com/ShuaiAlger/LGPR.

The rail surface defects directly impact the safety and efficiency of heavy-haul train operations. Timely assessment of these defects is crucial for informed maintenance decisions, with precise defect detection at its core. In recent years, the accumulation of extensive rail inspection images has led to the application of numerous computer vision-based methods for pixel-level detection of rail surface defects. However, given the constraint of a limited number of labeled defect samples, ensuring the generalization and robustness of existing methods remains challenging, particularly across varying track conditions and complex heavy-haul scenarios. Thus, this paper introduces a Segment-Anything-Model (SAM)-enhanced method for the detection of rail surface defects. First, a shadow-detection-based algorithm is developed to extract the rail regions and mitigate background interference. Then a student-teacher-Simi-network (S-T-Simi)-based unsupervised method is designed to generate prompt information for SAM. Utilizing this prompt information, we develop a task-specified SAM for precise rail defect detection. Finally, comprehensive validation is performed using inspection data collected from diverse heavy-haul tracks. Experimental results indicate that the proposed method achieves highly accurate segmentation of rail defects.

The operation of urban rail transit is inevitably affected by disturbances in practice, causing the original train timetable and rolling stock circulation to be infeasible. This paper addresses the real-time train rescheduling problem through a novel two-step optimization framework. To realign train operations with the original plan, the first step manages traffic flow by optimizing dispatching measures, including retiming, cancellation, short-turning, and backup rolling stock utilization. To maintain the highest possible service quality during the transition period, the second step introduces stop-skipping and further fine-tunes the train timetable. Both steps are formulated as mixed-integer nonlinear programming models, and the second-step model is heuristically decomposed. For computational tractability, mathematical models are transformed using some linearization techniques and then solved according to the prescribed procedure. Numerical experiments based on a small-scale case and real-world data of the Beijing Yizhuang Metro Line show that the proposed two-step optimization framework can satisfy the real-time requirements and outperform the current rescheduling method employed in the automatic train supervision system. Furthermore, the framework is proven to be applicable to different disturbance durations and locations.

Monocular Bird’s Eye View (BEV) semantic segmentation is critical for autonomous driving for its inherent advantages in spatial representation and downstream tasks. However, it is challenging to simultaneously learn view transformation and pixel-wise classification. Previous works suffer from non-flat region distortion, distant depth ambiguity, and visual occlusion. To address these aforementioned concerns, we propose dual-branch view transformation (DVT), a novel framework for monocular BEV semantic segmentation. Our method consists of: (i) A dual-branch view transformation to decouple features into flat region and non-flat region and process them independently. (ii) A depth-aware weighting method to make the model pay more attention to the distant depth. (iii) An auxiliary task to introduce more inductive biases to alleviate the inaccuracy caused by visual occlusion. Furthermore, we design a class-aware weighting method to address the class and size imbalance of datasets. Experimental results on nuScenes and KITTI-360 datasets demonstrate that DVT outperforms previous state-of-the-art (SOTA). Our codes are available at https://github.com/MrPicklesGG/DVT.

In the context of 3D scene perception tasks, the significance of 3D occupancy prediction has been progressively growing, aiming to forecast the occupancy state of voxels in a discrete 3D space. However, existing methods typically exhibit several limitations, such as restricted adaptability to non-pinhole cameras due to fixed camera parameters, heavy reliance on 3D annotations because of the inability to project 3D output back to the camera plane, and inferior real-time inference performance resulting from the conversion process from 2D to 3D features. To address these constrains, we introduce GenerOcc, a self-supervised framework of real-time 3D occupancy prediction for monocular generic cameras. We have collected the fisheye Dominant dataset to confirm the compatibility of our ray-based camera model with non-pinhole cameras. By transforming the occupancy prediction task into a depth estimation task in a self-supervised manner, we eliminate dependency on 3D annotations. Furthermore, we propose a parametric voxel probability distribution module that leverages 2D features to quickly predict 3D occupancy without 3D representations of the scene. Additionally, our GenerOcc has been extensively evaluated on public pinhole Occ3D-nuScenes dataset and our proprietary fisheye Dominant dataset, both yielding impressive performance.

In the emerging field of urban digital twins (UDTs), there are extensive and captivating opportunities for leveraging cutting-edge deep learning techniques. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark suite will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail profile faults are the most direct impact factors when considering the health conditions of rails. This paper puts forward rail fault diagnosis from two perspectives: quantifying the level of complexity and chaos of different profiles, and measuring the level of correlation between different profiles, which correspond to the newly proposed refined dispersion entropy (RDE) method and the correlation plane method, respectively. The RDE uses weighted-dispersion patterns to extract accurate time domain features from rail profile data, and the correlation plane can characterize nonlinear and non-monotonic relationships between analyzing subjects, which are the main contributions of this study. Experimental results with simulated and reality-based data show that the proposed methods can identify faulty profile data and discriminate different types of profile faults more effectively when compared with existing methods.

Great attention is paid on the smooth operation for long journey high-speed trains. And it is significant to recover to a stable state with disturbances such that the ride comfort of high-speed trains is ensured. This paper investigates a coordinated control approach of a high-speed train to reduce the interaction force between successive traction units. Firstly, the running resistance is assumed to be nonlinear and multiple traction units are connected by elastic couplings, then a multi-particle model is created. Next, a finite-time coordinated control protocol is designed based on the principle of multi-agent control and the finite-time Lyapunov stability theorem. The protocol is proved to be robust to disturbances and the speed consensus for all traction units is achieved. Meanwhile, the interaction forces between successive traction units converge to zero within a finite time. Finally, some numerical results are conducted to validate the proposed approach.

Train breakdowns have significant negative impacts on metro systems and passengers. In this context, the implementation of a train-to-train rescue serves as a crucial mechanism to restore operations promptly. This paper proposes a train rescheduling approach in the case of a train breakdown, incorporating a train-to-train rescue on a metro line. The train-to-train rescue procedure is formulated into two sub-models according to the depot position and the operating direction of the faulty train. The train timetable rescheduling and the rolling stock rescheduling problems are simultaneously considered in this paper by leveraging advanced rescheduling strategies such as the flexible short-turning and adding backup trains. Subsequently, a two-stage approach is developed to solve the model. Additionally, some valid inequalities are proposed to improve the lower bound of the model. Simulations are carried out using a case study based on the real-world data from the Beijing metro Yizhuang line to verify the effectiveness of the train rescheduling model. The proposed algorithms achieve high-quality solutions within a reasonable timeframe, surpassing the efficiency of the CPLEX optimizer.

"Search for" or "Navigate to"? When we find a specific object in an unknown environment, the two choices always arise in our subconscious mind. Before we see the target, we search for the target based on prior experience. Once we have seen the target, we can navigate to it by remembering the target location. However, recent object navigation methods consider using object association mostly to enhance the "search for" phase while neglecting the importance of the "navigate to" phase. Therefore, this paper proposes a dual adaptive thinking (DAT) method that flexibly adjusts thinking strategies in different navigation stages. Dual thinking includes both search thinking according to the object association ability and navigation thinking according to the target location ability. To make navigation thinking more effective, we design a target-oriented memory graph (TOMG) (which stores historical target information) and a target-aware multi-scale aggregator (TAMSA) (which encodes the relative position of the target). We assess our methods based on the AI2-Thor and RoboTHOR datasets. Compared with state-of-the-art (SOTA) methods, our approach significantly raises the overall success rate (SR) and success weighted by path length (SPL) while enhancing the agent’s performance in the "navigate to" phase.

Stereo matching is a common technique used in 3D perception, but transparent objects such as reflective and penetrable glass pose a challenge as their disparities are often estimated inaccurately. In this paper, we propose transparency-aware stereo (TA-Stereo), an effective solution to tackle this issue. TA-Stereo first utilizes a semantic segmentation or salient object detection network to identify transparent objects, and then homogenizes them to enable stereo matching algorithms to handle them as non-transparent objects. To validate the effectiveness of our proposed TA-Stereo strategy, we collect 260 images containing transparent objects from the KITTI Stereo 2012 and 2015 datasets and manually label pixel-level ground truth. We evaluate our strategy with six deep stereo networks and two types of transparent object detection methods. Our experiments demonstrate that TA-Stereo significantly improves the disparity accuracy of transparent objects. Our project webpage can be accessed at mias.group/TA-Stereo.

The energy-efficient train control (EETC) problem is investigated in this article. And a soft actor-critic (SAC)-based method is proposed to optimize the train driving strategy. First, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process, which can be solved by deep reinforcement learning algorithms. Second, an optimization method based on the SAC method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals.

This paper investigates the disturbance observer based robust cruise control problem for the heavy haul train with input saturation and disturbances. Both uncertain parameters and actuator saturation are taken into account in the dynamic model of the heavy haul train. To reject the influence of disturbances from the input channel, a linear disturbance observer is proposed to approximate the unknown disturbance, and an augmented system is constructed by combining the train state and the disturbance estimation error. According to the Lyapunov stability analysis method and the guaranteed cost control theory, a sufficient condition for the existence of the composite state-feedback control law and the disturbance observer parameter matrix are obtained, the coupler deviation and the disturbance estimation error are stable at the equilibrium point, and meanwhile the minimization of a given train performance index is ensured. Numerical experiments are provided to illustrate the effectiveness of the proposed approach.

Due to the inventible disruptions caused by e.g., flood, hurricane and blizzard, metro managers in recent years have gradually shifted their attention from prevention of disruptions to ability to withstand and quick recovery from these disruptions, hence the need for enhancing the resilience of an urban rail system. In this paper, we propose a resilience-oriented train rescheduling framework, which helps the rail transit system recover to the normal state as soon as possible in case of disruptions, with the help of pre-allocated rolling stocks at the depots, side tracks and timetable rescheduling of grains. Specifically, we first construct an event-activity network for an urban rail line with multiple depots and side tracks, in which the arrival and departure of trains are modeled as a set of events. Several groups of decision variables and linear constraints are denoted to model the rescheduling of trains. Considering the use of short-turning train rescheduling strategy and pre-allocated rolling stocks, we then formulate the problem into a mixed-integer linear programming (MILP) model, where the objective is to maximize the resilience of the urban rail line against disruptions. Through the analysis of model properties, we develop a branch-and-cut algorithm by deriving a series of linear inequalities, which we prove are valid inequalities, to strength the tightness of the MILP model. Finally, numerical experiments based on real-world data of Beijing metro are conducted to verify the effectiveness of our approach.

In this article, we study the optimal control problem of continuous-time (CT) time-invariant nonlinear systems with stochastic nonlinear disturbances. A new stochastic adaptive dynamic programming (ADP) method is developed to solve the Hamilton–Jacobi–Bellman equation (HJBE). Under the conditional expectation, the value function and the control law are successively approximated simultaneously. The asymptotic stability of the closed-loop stochastic system in probability is analyzed by the stochastic Lyapunov direct method, and the convergence of the developed ADP method is given. Finally, four simulations illustrate the effectiveness of the developed method.

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at sites.google.com/view/udtiri-workshop/bigdata-2022.

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort.

Learning and identifying key concepts from past fault records are essential for us to understand the causes of these faults, which lay the foundation for the fault diagnosis and prognosis. At present, faults in many fields (e.g., rail, automobile, and smart grid) are recorded in textual form. Due to the lack of effective mining and analysis tools, latent information in the massive textual data (text records) has not been fully unearthed. In this paper, a novel Adversarial Training-based Lattice LSTM model called AT-Lattice is proposed to address this problem. In this model, the Named Entity Recognition (NER) is achieved by Lattice LSTM and Conditional Random Field (CRF), where the Lattice LSTM is used to provide sequence information between words, and the CRF is used to get the final entity prediction result. In addition, the Chinese Word Segmentation (CWS) task is introduced to conduct the adversarial training with the NER task. The framework of the adversarial training is able to make full use of the boundary information and filter out the noise caused by the introduced CWS task. More importantly, extensive experiments are conducted on five different train fault datasets collected by a rail transit company. The results demonstrate that the proposed model outperforms the state-of-the-art baselines.

For enhancing the operation efficiency of the fully automatic operation (FAO) system in the urban rail transit (URT), this paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions. To better express the characteristic of the arriving passengers, the fuzzy passenger arrival rate is adopted to address the uncertainty of the passenger flow, and a T-S fuzzy state-space model is established to express the periodical movement of the train traffic in an URT loop line. By considering the possible packet dropout phenomenon during the wireless data transmissions, which may lead to the instability of the train traffic system and degrade the performance of the regulation strategy, a robust real-time train regulation strategy is developed based on the fuzzy predictive control theory, which distinguishes existing studies in that the uncertainty dropout rate is contemplated to address the complexity of the actual operation environment. A sufficient condition for the proposed control law is presented to guarantee that the nominal train schedule is recovered from disturbed situations with a given attenuation level by means of the$H_{\infty }$performance index, and meanwhile the optimization of the upper bound on the objective function balancing the service efficiency and control cost is achieved. Numerical simulations based on the Beijing subway loop line 2 are presented for demonstration of the effectiveness of the introduced strategy.

This paper explores the possibility of improving the verification of file-sharing qualification in Blockchain-based File-Sharing system. Previous work, such as Filecoin, proposes an interactive protocol for file storage scenario. It enables file-holders to prove that the file is stored in independent storage without disclosing the content of the file. However, it uses a heavy cryptographic machinery zk-SNARKs and needs a trusted third party to generate public parameters. This paper proposes an efficient file-sharing verification protocol: Verifications of File-Sharing Qualification (VoFSQ). VoFSQ allows file-holders to verify with each other whether the shared-file has been preserved for a period of time. Users who compete for file-sharing qualification need to spend a certain amount of computing and storage resources to generate the shared-files based evidence files. Take into the cost into account, users will prefer using idle hard disk to preserve the evidence file continuously other than delete and regenerate it. Compared to the protocol of Filecoin, VoFSQ is more efficient in the prover and verifier proof phases. Meanwhile, VoFSQ does not disclose the private information of shared-file, and does not rely on trusted third parties. This paper also implements and tests VoFSQ, experimental results show that protocol can be executed efficiently.

Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

BlockChain have seen many significant adoptions in many sectors because of its characteristics such as tamper-proof, easy traceable, decentralized and so on. The BlockChain could be considered as a super database that cannot be tampered with, any data that does not want to be tampered can be recorded on the blockchain. The characteristics of blockchain are very suitable for the scene of digital archives protection. This paper proposes a double-blockchains framework concerning the management of archives. The framework can concatenate different kinds of isolated digital archive management systems to provide a public and dependable archive platform. The double-blockchains technology is easily integrated into the digital archives management system by calling the public interfaces of this framework. At last, we give an implementation of this framework. The experiment shows that this framework can resolve the problems encountered in current digital archive management system.

This paper addresses the robust automatic train regulation problem in high-frequency metro lines with fuzzy passenger arrival rate. Due to the uncertainty of passenger demand, the passenger arrival rate is assumed to be represented by fuzzy variables. A nonlinear state-space model is formulated to describe the characteristic of metro train operation. To satisfy the real-time requirement of train regulation, a fuzzy constrained predictive control approach is designed to optimize a cost function at each decision epoch subject to safety constraints on the control input. Based on the Lyapunov stability theory and model predictive control method, sufficient conditions for the existence of corresponding state feedback control law are given in a set of linear matrix inequalities. Moreover, for reducing delays caused by the uncertain disturbance, the robust train regulation strategy is designed to guarantee that the practical train timetable tracks the nominal one with respect to certain disturbance attenuation level. The effectiveness of the proposed approach is validated by a number of experiments under real running circumstances of Beijing Metro Yizhuang Line of China.

The majority of the existing appearance-based vehicle-detection systems make use of a sliding-window paradigm for vehicle-candidate regions location. In order to locate all vehicle regions with various sizes and shapes, a large number of search windows are generated by a sliding-window paradigm in most vehicle-detection systems. It is desirable to obtain fewer and more precisely located vehicle candidate regions for further detection. For this purpose, a novel graph-based algorithm is proposed to locate the vehicle proposal regions, which estimates the possibility of a vehicle contained in a bounding box. Experimental results on the public traffic analysis data set (KITTI) and PASCAL VOC 2007 show that the proposed region proposal approach leads to better performances compared with popular bottom-up region proposal methods. Moreover, the proposed vehicle-detection system is evaluated on the KITTI data set, which are determined to be satisfactory, even for the images containing vehicles that have undergone scale variations and camera viewpoint changes, as well as for images that were photographed with complex backgrounds.

Increasing attention is being paid to energy efficiency in subway systems to reduce operational cost and carbon emissions. Optimization of the driving strategy and efficient utilization of regenerative energy are two effective methods to reduce the energy consumption for electric subway systems. Based on a common scenario that an accelerating train can reuse the regenerative energy from a braking train on the opposite track, this paper proposes a cooperative train control model to minimize the practical energy consumption, i.e., the difference between traction energy and the reused regenerative energy. First, we design a numerical algorithm to calculate the optimal driving strategy with the given trip time, in which the variable traction force, braking force, speed limits, and gradients are considered. Then, a cooperative train control model is formulated to adjust the departure time of the accelerating train for reducing the practical energy consumption during the trip by efficiently using the regenerative energy of the braking train. Furthermore, a bisection method is presented to solve the optimal departure time for an accelerating train. Finally, the optimal driving strategy is obtained for the accelerating train with the optimal departure time. Case studies based on the Yizhuang Line, Beijing Subway, China, are presented to illustrate the effectiveness of the proposed approach on energy saving.

Presently existing lightweight indoor/outdoor detection schemes on phones acquire accuracy by sensing variations of ambient physical environmental properties with inherent sensors on mobile phones, with which, however, the detection scheme cannot work well in some ambient environments, where the variations are not very observable. This detection scheme is with very high dependency on light. The I/O detector does not work well in poor lighting or fast changing lighting settings, therefore the I/O detection is very much challenged at times like dawn, dusk, or night. The target of this paper is finding a pervasive detection scheme independent of physical environments. In this paper, we present MobiIO, an lightweight indoor and outdoor detection scheme based on analyses of human activities. By recording human indoor and outdoor motion activities with sensors, typical features of their activities are extracted. We compare assorted combinations or groupings of various properties with SVM classifier. We classify indoor/outdoor settings through classifiers like SVM, Bayes, decision trees, HMM and compare the effects of classification in between various classifying algorithms.

Energy efficiency is paid more and more attention in railway systems for reducing the cost of operation companies and emissions to the environment. In subway systems, the optimizations on timetable and driving strategy are two important and closely dependent parts of energy-efficient operations. The former regulates the fleet size and the trip time at interstations, and the latter determines the control sequences of traction and braking force during the trip. Most conventional research optimized the timetable and the driving strategy separately such that global optimality cannot be achieved. In this paper, we analyze the hierarchy of energy-efficient train operation and then propose an integrated algorithm to generate the globally optimal operation schedule, which can get better energy-saving performance. Within the criteria of meeting the passenger demand, the integrated energy-efficient algorithm can simultaneously obtain the optimal timetable and driving strategy for trains, which realizes the combination of the high-level transportation management and the low-level train operation control. The simulation results based on the Beijing Yizhuang Subway Line illustrate that the integrated algorithm can achieve a 24.0% energy reduction for one day, on average. In addition, the computation time is within 2 s, which is short enough to be applied for real-time control system.

Given rising energy prices and environmental concerns, train energy-efficient operation techniques are paid more attention as one of the effective methods to reduce operation costs and energy consumption. Generally speaking, the energy-efficient operation technique includes two levels, which optimize the timetable and the speed profiles among successive stations, respectively. To achieve better performance, this paper proposes to optimize the integrated timetable, which includes both the timetable and the speed profiles. First, we provide an analytical formulation to calculate the optimal speed profile with fixed trip time for each section. Second, we design a numerical algorithm to distribute the total trip time among different sections and prove the optimality of the distribution algorithm. Furthermore, we extend the algorithm to generate the integrated timetable. Finally, we present some numerical examples based on the operation data from the Beijing Yizhuang subway line. The simulation results show that energy reduction for the entire route is 14.5%. The computation time for finding the optimal solution is 0.15 s, which implies that the algorithm is fast enough to be used in the automatic train operation (ATO) system for real-time control.