Systematic packaging design tools integrating functional and environmental consequences on product life cycle: Case studies on laundry detergent and milk

This study presents systematic packaging design tools integrating functional and environmental consequences on product life cycle. To design packaging for sustainability, the trade-offs between functional and environmental aspects of packaging throughout the product life cycle should be considered. However, it is difficult for packaging designers to understand the overall trade-offs because the extent of the design consequences on the entire life cycle of packaging and its contents is unclear. We developed two tools for packaging design: the Life Cycle Association Matrix (LCAM) and the Function Network Diagram (FND). The following three steps, based on literature reviews and interviews with industrial experts, were applied. Firstly, we listed the product functions and design variables related to the functions as the attributes allocated to the product life cycle. Secondly, the attributes were connected appropriately based on causal relationships. Lastly, we identified the factors to support decision making in the packaging design procedure. As a result, the LCAM depicts the design consequences on the life cycle, and the FND determines the stakeholders affected by the design consequences. Two case studies were demonstrated to analyze the trade-offs by using our tools. In the case studies, a liquid laundry detergent bottle and a milk carton were redesigned. The tools identified the design consequences and stakeholders affected by the redesign of the usability and protective function for the detergent and milk cases, respectively. The results showed the significance of understanding the design consequences on the product life cycle by integrating the functional and environmental aspects.     

Real-time measurement of pointing action by using DSP

This paper presents an approach of measuring in real-time the vector of finger that is pointing to an object. DSP is used in the operation processing unit in order to do the real-time processing. The steps include the extraction of flesh-colored regions from an image, the labeling of the flesh-colored regions, and the detection of two characteristic positions on the finger so that the direction that the finger is pointing at will be calculated. The entire process takes about 29 msec, which makes it possible to have the frame rate of 34 fps. With this frame rate, this measurement approach is considered real-time and promising to be merged into other application systems. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

An automatic torque ripple compensation system for direct drive motors

A serious problem in motion control is the occurrence of torque ripple. Since a direct drive (DD) motor with a magnet rotor uses a rare-earth magnet, the torque ripple is more serious than that of conventional servomotors. This paper presents an auto-compensation of torque ripple using a software-implemented torque observer. The frequency and amplitude of torque ripple can be obtained in the controller by using the estimated torque from an observer, and the autonomous generation of a compensating current component for a torque ripple is possible. The proposed algorithm has been verified by experiments.     

Radiation tolerance for the SMASH-series front-end chips for a silicon micro-strip detector

The radiation tolerance for prototype front-end chips designed for a silicon micro-strip detector was examined with a ⁶⁰Co irradiation source to establish an allowable range of the radiation dose. The irradiated front-end chips were SMA²SH-64A, a 64-channel preamplifier array; SMA²SH-1, a single-channel preamplifier circuit with a comparator; and Control-C, a digital-control chip for the preamplifiers.     

An empirical approach for structure-based prediction of carbohydrate-binding sites on proteins

A computer program system was developed to predict carbohydrate-bindingsites on three-dimensional (3D) protein structures. The programssearch for binding sites by referring to the empirical rulesderived from the known 3D structures of carbohydrate–proteincomplexes. A total of 80 non-redundant carbohydrate–proteincomplex structures were selected from the Protein Data Bankfor the empirical rule construction. The performance of theprediction system was tested on 50 known complex structuresto determine whether the system could detect the known bindingsites. The known monosaccharide-binding sites were detectedamong the best three predictions in 59% of the cases, whichcovered 69% of the polysaccharide-binding sites in the targetproteins, when the performance was evaluated by the overlapbetween residue patches of predicted and known binding sites. Received April 24, 2003; revised June 2, 2003; accepted June 10, 2003.     

Mutual learning of multi-consciousness agents,including an ego for an autonomous vehicle

In this paper, we propose a multi-agent learning system for the control of an intelligent robot, based on a model of the human consciousnesses, including the ego. We pay attention to the intelligent learning processes of human beings. We try to give a robot a high learning ability by modeling the roles of the human consciousnesses, including the ego. In most ordinary methods, the instructions for learning are given from outside the system only. In the proposed method, the instructions are given not only from outside, but also from inside (from other agents in the system). Therefore, the robot can learn efficiently because it has more instructions than usual. The learning is also more flexible, since an agent learns by instructions from other agents while the learning agent and one of the instructing agents exchange roles according to changes in the environment. We experimentally verified that the proposed method is efficient by using an actual robot.     

Detection of a brain aneurysm and a way to search for a brain aneurysm aimed at the development of a system to simulate an operation

In this article, a system is proposed for a simulated operation which would help a trainee surgeon to perform a medical operation to ensure that a cerebral aneurysm does not burst. The physician will have acquired empirical medical technologies from operations in traditional clinical teaching. However, there is a problem with safety and the burden to the patient. Therefore, recently a new training approach for a simulated medical operation using virtual reality has been explored. With the aim of developing a simulated system for a medical operation for a cerebral aneurysm, we considered the necessary functions such as detecting the brain aneurysm that is the target of the operation, and searching for a suitable blood vessel to make a plan for the operation.     

Construction of a robot control system which understands a voice and pointing actions

This research was aimed at making a robot that can go and get an object designated by the user. We produced a robot control system that uses pointing and voice control. This control system is composed of two systems. One system is the object instruction system that uses pointing, and the other is the object instruction system that uses voice. The approximate position of a designated object is recognized by the object instruction system that uses pointing, and information about the object and instructions about the operation are conveyed by the object instruction system that uses voice. This time, the object instruction system that uses pointing has been designed and verified as the first step. A calculation resource to calculate the approximate position of the designated object is obtained when the user points directly at the object. A robot can be moved around a designated object by using this system. The object instruction system that uses voice will be constructed in future work.     

Improvement of a neural network-based motion generator with bimanual coordination for upper limb prosthesis

We propose to redesign a neural network used as a motion generator with bimanual coordination for upper limb prosthesis in order to improve its learning capability. We assumed that the wearer of the prosthesis was a unilateral amputee. In our previous work, we proposed a prosthesis control system using a neural network that learned bimanual coordination in order to implement smooth motion with both hands. However, the previously proposed system has the problem that a neural network cannot generate the desired motion of the prosthesis in special cases. The reason is that the motion generator calculates the desired posture of the prosthesis from the current posture of the healthy arm only, regardless of the current posture of the prosthesis. We propose to use the current posture of both the healthy arm and the prosthesis as neural network inputs in order to solve this problem. In this article, we show that a single neural network whose input was the current posture of both arms could learn the relationships of the coordinated motions of holding boxes of different sizes, and the newly proposed system can calculate the desired motion of the prosthesis in special cases through computer simulations.