Double-threshold Strategy Research Articles

AdapTrack: An adaptive FairMOT tracking method applicable to marine ship targets

Ship tracking at sea is faced with the disadvantages of complex sea conditions and the large influence of ship occlusion on the tracker. Therefore, we propose a method called AdapTrack based on On the Fairness of Detection and Re-Identification in Multiple Object Tracking (FairMOT) which is suitable for marine targets. The search strategy of trivial augmentation is used to randomly select suitable data augmentation methods and strengths. Then, based on the FairMOT tracking framework, we change the sampling selection method of positive and negative samples from a two-dimensional Gaussian distribution with the same variances to a two-dimensional Gaussian distribution with different variances. It is limited by the bounding box (bbox) of ground truth. This method can improve the detection algorithm’s fitness to the ship target. At the same time, we use Multi-Object Tracking by Associating Every Detection Box (ByteTrack)’s double-threshold strategy to divide detection bboxes, which improves the matching and inference speed. In the first stage of data association, the high-scoring bbox calculates the cost matrix of data association through the Re-identification (Re-ID) model. In the second stage, the Intersection over Union(IOU) cost matrix is calculated after merging low-scoring detection bboxes and unmatched detection bboxes of the first stage. The method achieves Multiple Object Tracking Accuracy (MOTA) of 36.1, Identification F-Score (IDF1) of 47.3, and Frames Per Second (FPS) of 28.79 on the Singapore-Marine-dataset. Experiments show that this method can better alleviate Identification Switch (ID-switch) and ensure the real-time tracking of complex and changeable ship target tracking at sea.

A Two-stage Stochastic Programming for AGV scheduling with random tasks and battery swapping in automated container terminals

Automated guided vehicle (AGV) is one of the main equipment for horizontal transportation in automated container terminals, and the optimization of AGV scheduling has become increasingly important. Existing scheduling systems tend to make decisions based on deterministic conditions, ignoring the dynamic changes and uncertainties of the terminal environment, such as the arrival of random tasks during AGV operations. In addition, the battery swapping process is neglected in most AGV scheduling studies, yet it is crucial to ensure the operation of AGVs. In this paper, we construct a two-stage stochastic programming model for the joint scheduling problem of battery swapping and task operation with random tasks. A double-threshold constraint for battery swapping decision-making is adopted. The results show that the double-threshold strategy is better for AGV utilization than the single-threshold one. Upon the solution method, a simulation-based ant colony optimization algorithm is proposed. Sample average approximation is used to calculate the expected cost, and two local search procedures are introduced to improve the quality of the solutions. In the cases of multiple instances and several random task samples with different arrival rates, our method was compared with three practical policies under a deterministic model. Computational results show that the scheduling scheme considering random tasks in advance is more robust and stable.

The effect of information on queue-scalping service systems

We consider an M/M/1 queueing system with a single queue scalper who makes profit by selling his position in the queue. The equilibrium purchasing strategies of customers under observable case (with double-threshold strategy) and unobservable case (with mixed strategy) are derived. The profits of the queue scalper are obtained under two information levels. Numerical experiments suggest that the queue scalper is better off with unobservable case when the system is too congested or the operating cost is too low.

Intelligent prediction of HER2 status based on breast histopathology

Objective: To study the association between histopathological features and HER2 overexpression/amplification in breast cancers using deep learning algorithms. Methods: A total of 345 HE-stained slides of breast cancer from 2012 to 2018 were collected at the China-Japan Friendship Hospital, Beijing, China. All samples had accurate diagnosis results of HER2 which were classified into one of the 4 HER2 expression levels (0, 1+, 2+, 3+). After digitalization, 204 slides were used for weakly supervised model training, and 141 used for model testing. In the training process, the regions of interest were extracted through cancer detected model and then input to the weakly supervised classification model to tune the model parameters. In the testing phase, we compared performance of the single- and double-threshold strategies to assess the role of the double-threshold strategy in clinical practice. Results: Under the single-threshold strategy, the deep learning model had a sensitivity of 81.6% and a specificity of 42.1%, with the AUC of 0.67 [95% confidence intervals (0.560,0.778)]. Using the double-threshold strategy, the model achieved a sensitivity of 96.3% and a specificity of 89.5%. Conclusions: Using HE-stained histopathological slides alone, the deep learning technology could predict the HER2 status using breast cancer slides, with a satisfactory accuracy. Based on the double-threshold strategy, a large number of samples could be screened with high sensitivity and specificity.

SEMG-Based Human-in-the-Loop Control of Elbow Assistive Robots for Physical Tasks and Muscle Strength Training

In this letter we present a sEMG-driven human-in-the-loop (HITL) control designed to allow an assistive robot produce proper support forces for both muscular effort compensations, i.e. for assistance in physical tasks, and muscular effort generations, i.e. for the application in muscle strength training exercises related to the elbow joint. By employing our control strategy based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that our approach can be successfully used to achieve a target, quantifiable muscle activity assistance. In this relation, an experimental concept validation was carried out involving four healthy subjects in physical and muscle strength training tasks, reporting with single-subject and global results that the proposed sEMG-driven control strategy was able to successfully limit the elbow muscular activity to an arbitrary level for effort compensation objectives, and to impose a lower bound to the sEMG signals during effort generation goals. Also, a subjective qualitative evaluation of the robotic assistance was carried out by means of a questionnaire. The obtained results open future possibilities for a simplified usage of the sEMG measurements to obtain a target, quantitatively defined, robot assistance for human joints and muscles.

A sEMG-Driven Soft ExoSuit Based on Twisted String Actuators for Elbow Assistive Applications

The scope of this letter is to show the applicability of the Twisted String Actuators (TSAs) for lightweight, wearable and assistive robotic applications. To this aim, we have developed a novel surface electromyography (sEMG)-driven soft ExoSuit using the TSAs to perform both single and dual-arm elbow assistive applications. The proposed ExoSuit, with an overall weight of 1650 g, uses a pair of TSAs mounted in the back of the user, connected via tendons to the user's forearms to actuate each arm independently for supporting external loads. We confirm this new light-weight and customizable wearable solution via multiple user studies based on the biceps and tricep' sEMG measurements. We demonstrate that user's muscles can automatically activate and regulate the TSAs and compensate for the user's effort: by using our controller based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that the system was able to limit the biceps' sEMG activity under an arbitrary target threshold, compensating a muscular activity equal to 220% (related to a single arm 3 kg load) and 110% (related to a dual arm 4 kg load) of the threshold value itself. Moreover, the triceps' sEMG signal detects the external load and, depending on the threshold, returns the system to the initial state where it requires no assistance from the ExoSuit. The experimental results show the proposed ExoSuit's capabilities in both single and dual-arm load compensation tasks. Therefore, the applicability of the TSAs is experimentally demonstrated for a real-case assistive device, fostering future studies and developments of this kind of actuation strategy for wearable robotic systems.

On non-equilibria threshold strategies in ticket queues

In many real-life queueing systems, a customer may balk upon arrival at a queueing system, but other customers become aware of it only at the time the balking customer was to start service. Naturally, the balking is an outcome of the queue length, and the decision is based on a threshold. Yet the inspected queue length contains customers who balked. In this work, we consider a Markovian queue with infinite capacity and with customers that are homogeneous with respect to their cost reward functions. We show that that no threshold strategy can be a Nash equilibrium strategy. Furthermore, we show that for any threshold strategy adopted by all, the individual's best response is a double threshold strategy. That is, join if and only if one of the following is true: (i) the inspected queue length is smaller than one threshold, or (ii) the inspected queue length is larger than a second threshold. Our model is under the assumption that the response time of the server when he finds out that a customer balked is negligible. We also discuss the validity of the result when the response time is not negligible.

Pricing and referrals in diffusion on networks

When a new product or technology is introduced, potential consumers can learn its quality by trying it, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to study the adoption of technologies of uncertain value, when agents are connected by a network and a monopolist seller chooses a profit-maximizing policy. Consumers with low degree (few friends) have incentives to adopt early, while consumers with high degree have incentives to free ride. The seller can induce high-degree consumers to adopt early by offering referral incentives – rewards to early adopters whose friends buy in the second period. Referral incentives thus lead to a ‘double-threshold strategy’ by which low and high-degree agents adopt the product early while middle-degree agents wait. We show that referral incentives are optimal on certain networks while inter-temporal price discrimination is optimal on others.

A Dynamic Network Game for the Adoption of New Technologies

When a new product or technology is introduced, potential consumers can learn its quality by trying the product, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to study the adoption of technologies of uncertain value, when gents are connected by a network and a monopolist seller chooses a policy to maximize profits. Consumers with low degree (few friends) have incentives to adopt early, while consumers with high degree have incentives to free ride. The seller can induce high-degree consumers to adopt early by offering referral incentives - rewards to early adopters whose friends buy in the second period. Referral incentives thus lead to a `double-threshold strategy' by which low and high-degree agents adopt the product early while middle-degree agents wait. We show that referral incentives are optimal on certain networks while inter-temporal price discrimination (i.e., a first-period price discount) is optimal on others, and discuss welfare implications.

Model-based pairs trading in the bitcoin markets

We propose an optimal dynamic pairs trading strategy model for a portfolio of cointegrated assets. Using stochastic control techniques, we compute analytically the optimal portfolio weights and relate our result to several other strategies commonly used by practitioners, including the static double-threshold strategy. Finally, we apply our model to a bitcoin portfolio and conduct an out-of-sample test with historical data from three exchanges, with two cointegrating relations.

Detection of building changes from aerial images and light detection and ranging (LIDAR) data

Building models are built to provide three-dimensional (3-D) spatial information, which is needed in a variety of applications including city planning, construction management, location-based services of urban infrastructures, and the like. However, 3-D building models have to be updated on a timely manner to meet the changing demand. Rather than reconstructing building models for the entire area, it would be more convenient and effective to only update parts of the areas where there were changes. This paper aims at developing a new method, namely double-threshold strategy, to find such changes within 3-D building models in the region of interest with the aid of light detection and ranging (LIDAR) data. The proposed modeling scheme comprises three steps, namely, data pre-processing, change detection in building areas, and validation. In the first step for data pre-processing, data registration was carried out based on multi-source data. The second step for data pre-processing requires using the triangulation of an irregular network of data points collected by Light Detection And Ranging (LIDAR), focusing on those locations containing walls or other above-ground objects that were ever removed. Then, change detection in the building models can be made possible for finding differences in height by comparing the LIDAR point measurements and the estimates of the building models. The results may be further refined using spectral and feature information collected from aerial imagery. A double-threshold strategy was applied to cope with the highly sensitive thresholding often encountered when using the rule-based approach. Finally, ground truth data were used for model validation. Research findings clearly indicate that the double-threshold strategy improves the overall accuracy from 93.1% to 95.9%.

Changes in gamma-band power and phase synchronization with the difficulty of a visual oddball task

We examined local and global differences in neuronal association patterns due to task difficulty. Induced and evoked gamma-band activity (GBA) was investigated using electroencephalograms recorded during visual oddball tasks of two difficulty levels. Significant phase synchronization (PS) in the gamma-band was determined by a double-threshold strategy, devised to detect prominent increases with respect to prestimulus baseline and surrogate data. A considerable increase in the early evoked GBA was observed for both difficulties, and this tended to be delayed for the hard task. Induced GBA was increased at the P300 latency period, but no difficulty-related change was observed. Intense PS among anterior and posterior regions along the midline was observed in a 30–38 Hz band, which was enhanced in the easy task throughout task execution, although the overall pattern of connectivity was similar for both difficulties. This may be the underlying basis of the faster and more accurate response to the easy task. The results showed that the most apparent change in the gamma-band associated with difficulty was observed in the PS pattern, not in the spectral power itself. This may imply that the difficulty-related change in cortical information processing was implemented as a change in the spatiotemporal pattern of interregional connectivity.